{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multipole expansion for electromagnetic scattering\n",
    "\n",
    "Scattering of subwavelength particles is an important tool for manipulating beam profiles, enabling a range of innovative photonic applications, such as flat lenses, negative index materials, perfect dielectric absorbers, and transparent metamaterials.\n",
    "\n",
    "In many of these applications, decomposing the scattering cross section of a particle into its multipole components is invaluable for understanding and tailoring the behavior of the structure. While Mie theory provides an analytical expression for spherical particles, it is limited to spheres. For particles of arbitrary geometries, the exact multipole expansion can be obtained by numerically solving the electric field distribution of the particle and applying the model developed in the work by Rasoul Alaee, Carsten Rockstuhl and I. Fernandez-Corbaton , entitled \"An electromagnetic multipole expansion beyond the long-wavelength approximation\". [DOI: https://doi.org/10.1016/j.optcom.2017.08.064](https://doi.org/10.1016/j.optcom.2017.08.064).\n",
    "\n",
    "In this notebook, we will implement the multipole expansion for a nanosphere, allowing us to compare the results with the analytical solution using Mie theory. Nevertheless, it is straightforward to adapt this notebook for an arbitrary geometry. The model consists of a plane wave incident on a nanoparticle, with field and permittivity monitors set up to record the necessary quantities.\n",
    "\n",
    "For more information, please refer to our [learning center](https://www.flexcompute.com/tidy3d/examples/), where you can find tutorials and examples on how to analyze [plasmonic](https://www.flexcompute.com/tidy3d/examples/notebooks/PlasmonicNanoparticle/) and [dielectric](https://www.flexcompute.com/tidy3d/examples/notebooks/Near2FarSphereRCS/) nanoparticles.\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. \n",
    "\n",
    "FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it. \n",
    "\n",
    "<img src=\"img/multipoleExpansion.png\" width=\"400\" alt=\"ME\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:32:34.492284Z",
     "iopub.status.busy": "2025-05-15T10:32:34.492196Z",
     "iopub.status.idle": "2025-05-15T10:32:36.204518Z",
     "shell.execute_reply": "2025-05-15T10:32:36.204002Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation setup\n",
    "\n",
    "First we will define the basic parameters.\n",
    "A higher value for `n_frequencies` parameter yields a more smooth dataset at the cost of high memory consumption during the post-processing.\n",
    "Another important parameter is the `resolution`, which is defined here as the number of grid points to discretize the sphere in each direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:32:36.206837Z",
     "iopub.status.busy": "2025-05-15T10:32:36.206605Z",
     "iopub.status.idle": "2025-05-15T10:32:36.210085Z",
     "shell.execute_reply": "2025-05-15T10:32:36.209727Z"
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   "outputs": [],
   "source": [
    "# structure parameters\n",
    "radius = 0.18\n",
    "\n",
    "# sphere geometry object\n",
    "geometry = td.Sphere(center=(0, 0, 0), radius=radius)\n",
    "\n",
    "# define the height of the structure\n",
    "structure_height = 2 * radius\n",
    "\n",
    "# number of frequencies. A larger number will result in smoother data,\n",
    "# but will consume more memory during post-processing\n",
    "n_frequencies = 101\n",
    "\n",
    "\n",
    "# size of the structure\n",
    "lx = 2 * radius\n",
    "ly = 2 * radius\n",
    "lz = structure_height\n",
    "\n",
    "# resolution. Since we will use a mesh override around the structure,\n",
    "# the default value of 10 is sufficient\n",
    "min_steps_per_wvl = 10\n",
    "\n",
    "# target spectrum\n",
    "wl1 = 0.55\n",
    "wl2 = 1.4\n",
    "\n",
    "# converting to frequency\n",
    "central_wl = (wl2 - wl1) / 2 + wl1\n",
    "freq1 = td.C_0 / wl1\n",
    "freq2 = td.C_0 / wl2\n",
    "freq0 = (freq1 + freq2) / 2\n",
    "fwidth = (freq1 - freq2) / 2\n",
    "\n",
    "# frequencies to be analyzed by the field monitor\n",
    "wls = np.linspace(wl2, wl1, n_frequencies)\n",
    "freqs = td.C_0 / wls\n",
    "\n",
    "# number of grid points to discretize the structure\n",
    "resolution = 60\n",
    "\n",
    "# simulation runtime\n",
    "run_time = 1e-12\n",
    "\n",
    "# simulation domain size. We add the central wavelength to ensure proper distance from the PML\n",
    "Lx = central_wl + lx\n",
    "Ly = Lx\n",
    "Lz = central_wl + lz\n",
    "\n",
    "# material for the structure and substrate\n",
    "structure_medium = td.material_library[\"cSi\"][\"Green2008\"]\n",
    "substrate_material = td.Medium(permittivity=1)\n",
    "\n",
    "# position of the source\n",
    "source_z = -structure_height / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will define the simulation object.\n",
    "\n",
    "Note that, for accurate results, we will set `colocate = False` for the `FieldMonitor` object. This means that the fields will be stored at their position in the Yee lattice grid, without interpolation. Since the multipole expansion is sensitive to fields at the surface, this option yields more accurate results for this analysis.\n",
    "\n",
    "We will also use the [TFSF](https://docs.flexcompute.com/projects/tidy3d/en/stable/api/_autosummary/tidy3d.TFSF.html) source. This source injects exactly $1\\frac{\\text{W}}{\\mu\\text{m}^2}$. Hence, using the Poynting theorem, we know the relation of intensity and electric field: $I = \\frac{1}{2}cn\\epsilon_0|E_0|^2 = 1$, hence $|E_0|^2 = \\frac{2}{cn\\epsilon_0}$ where $n$ is the background medium index, which is 1 in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:32:36.211696Z",
     "iopub.status.busy": "2025-05-15T10:32:36.211400Z",
     "iopub.status.idle": "2025-05-15T10:32:36.221917Z",
     "shell.execute_reply": "2025-05-15T10:32:36.221547Z"
    }
   },
   "outputs": [],
   "source": [
    "# adding PMLs\n",
    "boundary_spec = td.BoundarySpec(x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml())\n",
    "\n",
    "# TFSF source\n",
    "source = td.TFSF(\n",
    "    center=(geometry.center[0], geometry.center[1], -central_wl / 16),\n",
    "    size=(\n",
    "        lx * 1.2,\n",
    "        ly * 1.2,\n",
    "        lz + central_wl / 8 + 0.1,\n",
    "    ),  # add 100 nm to avoid touching the structure\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"+\",\n",
    "    injection_axis=2,\n",
    "    pol_angle=0,\n",
    "    angle_theta=0,\n",
    "    angle_phi=0,\n",
    ")\n",
    "\n",
    "# since the structure is smaller than the target wavelength,\n",
    "# a finer mesh is required to ensure accurate results\n",
    "structure_override = td.MeshOverrideStructure(\n",
    "    geometry=td.Box(center=source.center, size=source.size),\n",
    "    dl=(min(lx, ly, lz) / resolution,) * 3,\n",
    ")\n",
    "\n",
    "mesh_override = [structure_override]\n",
    "\n",
    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, override_structures=mesh_override)\n",
    "\n",
    "# defining the structure objects\n",
    "structure = td.Structure(\n",
    "    geometry=geometry,\n",
    "    medium=structure_medium,\n",
    ")\n",
    "\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-td.inf, -td.inf, -999),\n",
    "        rmax=(td.inf, td.inf, 0 - structure_height / 2),\n",
    "    ),\n",
    "    medium=substrate_material,\n",
    ")\n",
    "\n",
    "# field monitor for calculating the fields\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=geometry.center,\n",
    "    fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
    "    size=(lx * 1.1, ly * 1.1, lz * 1.1),\n",
    "    freqs=freqs,\n",
    "    name=\"fieldMon\",\n",
    "    colocate=False,\n",
    ")\n",
    "\n",
    "# permittivity monitor\n",
    "permittivity_monitor = td.PermittivityMonitor(\n",
    "    center=field_monitor.center,\n",
    "    size=field_monitor.size,\n",
    "    freqs=field_monitor.freqs,\n",
    "    name=\"epsMon\",\n",
    ")\n",
    "\n",
    "# creating the simulation object\n",
    "sim = td.Simulation(\n",
    "    size=(Lx, Ly, Lz),\n",
    "    structures=[substrate, structure],\n",
    "    sources=[source],\n",
    "    monitors=[field_monitor, permittivity_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    "    grid_spec=grid_spec,\n",
    "    shutoff=1e-7,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot the simulation to assert if everything is correct, check for the estimated cost, and run the simulation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:32:36.223431Z",
     "iopub.status.busy": "2025-05-15T10:32:36.223269Z",
     "iopub.status.idle": "2025-05-15T10:32:36.247337Z",
     "shell.execute_reply": "2025-05-15T10:32:36.246369Z"
    }
   },
   "outputs": [
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    "sim.plot_3d()\n",
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    "plt.show()"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:32:36.288604Z",
     "iopub.status.busy": "2025-05-15T10:32:36.288158Z",
     "iopub.status.idle": "2025-05-15T10:32:40.177912Z",
     "shell.execute_reply": "2025-05-15T10:32:40.177573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:19:22 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'ME'</span> with task_id                                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:19:22 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'ME'\u001b[0m with task_id                                     \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">f-4937-9e57-a0d5e4b8f93b'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=601213;https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=136576;https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=601213;https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=405648;https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=601213;https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b\u001b\\\u001b[32m-76a5c2d9-6a4\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=601213;https://tidy3d.simulation.cloud/workbench?taskId=fdve-76a5c2d9-6a4f-4937-9e57-a0d5e4b8f93b\u001b\\\u001b[32mf-4937-9e57-a0d5e4b8f93b'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-2d594d1c-257b-417d-92d0-e6dcb36d63f0\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=253174;https://tidy3d.simulation.cloud/folders/folder-2d594d1c-257b-417d-92d0-e6dcb36d63f0\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56996013c3d3436e96d78895e73c4ec7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:19:24 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.138</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:19:24 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.138\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:19:25 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.138</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:19:25 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.138\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.13767145817929347"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_id = web.upload(sim, \"ME\")\n",
    "web.estimate_cost(task_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:32:40.184411Z",
     "iopub.status.busy": "2025-05-15T10:32:40.184346Z",
     "iopub.status.idle": "2025-05-15T10:34:03.596894Z",
     "shell.execute_reply": "2025-05-15T10:34:03.596292Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'ME_Si_Sphere'</span> with task_id                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-00133ca7-69bc-4649-bedd-dadd14982297'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'ME_Si_Sphere'\u001b[0m with task_id                           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-00133ca7-69bc-4649-bedd-dadd14982297'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69bc-4649-bedd-dadd14982297\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69b</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69bc-4649-bedd-dadd14982297\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c-4649-bedd-dadd14982297'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=342113;https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69bc-4649-bedd-dadd14982297\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=124625;https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69bc-4649-bedd-dadd14982297\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=342113;https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69bc-4649-bedd-dadd14982297\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=36348;https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69bc-4649-bedd-dadd14982297\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=342113;https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69bc-4649-bedd-dadd14982297\u001b\\\u001b[32m-00133ca7-69b\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=342113;https://tidy3d.simulation.cloud/workbench?taskId=fdve-00133ca7-69bc-4649-bedd-dadd14982297\u001b\\\u001b[32mc-4649-bedd-dadd14982297'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-b3152221-3aed-432b-9442-3f044d9b648e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'MultipoleExpansion'</span></a>.                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=568549;https://tidy3d.simulation.cloud/folders/folder-b3152221-3aed-432b-9442-3f044d9b648e\u001b\\\u001b[32m'MultipoleExpansion'\u001b[0m\u001b]8;;\u001b\\.                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c44abac8bea34f7c9961416e60b98602",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:19:28 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.138</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:19:28 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.138\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:19:29 -03 </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:19:29 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:21:02 -03 </span>loading simulation from ME.hdf5                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:21:02 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from ME.hdf5                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(\n",
    "    simulation=sim,\n",
    "    task_name=\"ME_Si_Sphere\",\n",
    "    path=\"ME.hdf5\",\n",
    "    folder_name=\"MultipoleExpansion\",\n",
    ")\n",
    "\n",
    "# sim_data = td.SimulationData.from_file('ME.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will define the function for calculating the multipole expansion, given the electric field and permittivity data, as demonstrated in the [paper](https://doi.org/10.1016/j.optcom.2017.08.064).\n",
    "\n",
    "The expressions for the multipole moments are as follows:\n",
    "\n",
    "\n",
    "**Electric dipole:**\n",
    "\n",
    "$p_{\\alpha}=-\\frac{i}{\\omega}\\bigg\\{\\int d^{3}\\vec{r}J_{\\alpha}^{\\omega}j_{0}(kr)+\\frac{k^{2}}{2}\\int d^{3}\\vec{r}\\bigg[3(\\vec{r}.\\vec{J_{\\omega}})r_{\\alpha}J_{\\alpha}^{\\omega}\\bigg]\\frac{j_{2}(kr)}{(kr)^{2}}\\bigg\\}$ \n",
    "\n",
    "**Magnetic dipole:**\n",
    "\n",
    "$m_{\\alpha}=\\frac{3}{2}\\int d^{3}\\vec{r}(\\vec{r}\\times\\vec{J})_{\\alpha}\\frac{j_{1}(kr)}{kr}$ \n",
    "\n",
    "**Electric quadrupole:**\n",
    "\n",
    "$Q_{\\alpha\\beta}^{e}=\\frac{3i}{\\omega}\\bigg\\{\\int d^{3}\\vec{r}\\bigg[3(r_{\\beta}J_{\\alpha}^{\\omega}+r_{\\alpha}J_{\\beta}^{\\omega})-2(\\vec{r}.\\vec{J_{\\omega}})\\delta_{\\alpha\\beta}\\bigg]\\frac{j_{1}(kr)}{kr}+2k^{2}\\int d^{3}\\vec{r}\\bigg[5r_{\\alpha}r_{\\beta}(\\vec{r}.\\vec{J_{\\omega}})-(r_{\\beta}J_{\\alpha}^{\\omega}+r_{\\alpha}J_{\\beta}^{\\omega})r^{2}-r^{2}(\\vec{r}.\\vec{J_{\\omega}})\\delta_{\\alpha\\beta}\\bigg]\\frac{j_{3}(kr)}{(kr)^{3}}\\bigg\\}$ \n",
    "\n",
    "**Magnetic quadrupole:**\n",
    "\n",
    "$Q_{\\alpha\\beta}^{m}=15\\int d^{3}\\vec{r}\\bigg\\{\\vec{r_{\\alpha}}(\\vec{r}\\times\\vec{J})_{\\beta}+\\vec{r_{\\beta}}(\\vec{r}\\times\\vec{J})_{\\alpha}\\bigg\\}\\frac{j_{2}(kr)}{(kr)^{2}}$ \n",
    "\n",
    "Where $J$ is the electric current density, given by:\n",
    "\n",
    "$J_{\\omega}(\\vec{r})=-i\\omega\\epsilon_{0}(\\epsilon_{r}-1)E_{\\omega}(\\vec{r})$\n",
    "\n",
    "Here, $\\alpha$ and $\\beta$ represent the coordinates, $\\vec{r}$ the position vector, and $j_i$ the spherical Bessel functions. \n",
    "\n",
    "Finally, the total scattering cross-section is given by the moments as follows:\n",
    "\n",
    "$C_{sca}=C_{sca}^{p}+C_{sca}^{m}+C_{sca}^{Q^{e}}+C_{sca}^{Q^{m}}+...=\\frac{k^{4}}{6\\pi\\epsilon_{0}^{2}|E_{0}|^{2}}\\bigg[\\sum_{\\alpha}\\bigg(|p_{\\alpha}|^{2}+\\bigg|\\frac{m_{\\alpha}}{c}\\bigg|^{2}\\bigg)+\\frac{1}{120}\\sum_{\\alpha\\beta}\\bigg(|kQ_{\\alpha\\beta}^{e}|^{2}+\\bigg|\\frac{Q_{\\alpha\\beta}^{m}}{c}\\bigg|^{2}\\bigg)+...\\bigg]$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:34:04.286043Z",
     "iopub.status.busy": "2025-05-15T10:34:04.285936Z",
     "iopub.status.idle": "2025-05-15T10:34:04.292997Z",
     "shell.execute_reply": "2025-05-15T10:34:04.292717Z"
    }
   },
   "outputs": [],
   "source": [
    "def ME(Ex, Ey, Ez, x, y, z, eps_xx, eps_yy, eps_zz, freqs, eps_medium=1):\n",
    "    # first we create the 4-dimensional (x,y,z,f) datasets\n",
    "    X, Y, Z, Freqs = np.meshgrid(x, y, z, freqs, indexing=\"ij\")\n",
    "\n",
    "    # defining the angular frequencies\n",
    "    omega = 2 * np.pi * freqs\n",
    "\n",
    "    # import libraries to carry out integration and Bessel functions\n",
    "    from scipy.integrate import trapezoid as trapz\n",
    "    from scipy.special import spherical_jn as jn\n",
    "\n",
    "    # function for calculating the current density\n",
    "    J = lambda Ei, epsilon: 1j * (2 * np.pi * Freqs) * td.EPSILON_0 * (epsilon - eps_medium) * Ei\n",
    "\n",
    "    # function for calculating volume integral\n",
    "    integrate = lambda Data: trapz(trapz(trapz(Data, x=x, axis=0), x=y, axis=0), x=z, axis=0)\n",
    "\n",
    "    # defining the current densities\n",
    "    Jx = J(Ex, eps_xx)\n",
    "    Jy = J(Ey, eps_yy)\n",
    "    Jz = J(Ez, eps_zz)\n",
    "\n",
    "    # r vector\n",
    "    r = np.sqrt(X**2 + Y**2 + Z**2)\n",
    "\n",
    "    # wavevector\n",
    "    k = omega / td.C_0\n",
    "\n",
    "    # dot product k.r\n",
    "    kr = k * r\n",
    "\n",
    "    # dot product r.J\n",
    "    rj = X * Jx + Y * Jy + Z * Jz\n",
    "\n",
    "    # function for calculating dipole moments\n",
    "    P = lambda Ji, Xi: (\n",
    "        (1j / omega)\n",
    "        * (\n",
    "            integrate(Ji * jn(0, kr))\n",
    "            + ((k**2) / 2) * integrate((3 * rj * Xi - Ji * r**2) * jn(2, kr) / kr**2)\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # dipole moments\n",
    "    Px = P(Jx, X)\n",
    "    Py = P(Jy, Y)\n",
    "    Pz = P(Jz, Z)\n",
    "\n",
    "    Ed = np.abs(Px) ** 2 + np.abs(Py) ** 2 + np.abs(Pz) ** 2\n",
    "\n",
    "    # cross product for calculating magnetic moments\n",
    "    rXJ_x = Y * Jz - Z * Jy\n",
    "    rXJ_y = Z * Jx - X * Jz\n",
    "    rXJ_z = X * Jy - Y * Jx\n",
    "\n",
    "    # dipole magnetic moments\n",
    "    Mx = (3 / 2) * integrate(rXJ_x * jn(1, kr) / kr)\n",
    "    My = (3 / 2) * integrate(rXJ_y * jn(1, kr) / kr)\n",
    "    Mz = (3 / 2) * integrate(rXJ_z * jn(1, kr) / kr)\n",
    "\n",
    "    Md = np.abs(Mx) ** 2 + np.abs(My) ** 2 + np.abs(Mz) ** 2\n",
    "\n",
    "    # auxiliary lists for calculating quadrupoles\n",
    "    coords = [X, Y, Z]\n",
    "    crossProd = [rXJ_x, rXJ_y, rXJ_z]\n",
    "    Js = [Jx, Jy, Jz]\n",
    "\n",
    "    # electric quadrupole\n",
    "    Eq = np.zeros(Px.shape)\n",
    "    for alpha in range(3):\n",
    "        for beta in range(3):\n",
    "            ra = coords[alpha]\n",
    "            rb = coords[beta]\n",
    "            ja = Js[alpha]\n",
    "            jb = Js[beta]\n",
    "\n",
    "            delta = 0 if alpha != beta else 1\n",
    "\n",
    "            integrand1 = (3 * (ra * jb + rb * ja) - 2 * rj * delta) * (jn(1, kr) / kr)\n",
    "            integrand2 = ((5 * ra * rb * rj) - (ra * jb + rb * ja) * r**2 - (r**2 * rj * delta)) * (\n",
    "                jn(3, kr) / kr**3\n",
    "            )\n",
    "\n",
    "            Eq_ab = (3j / omega) * (integrate(integrand1) + (2 * k**2) * integrate(integrand2))\n",
    "\n",
    "            Eq += (1 / 120) * np.abs(k * Eq_ab) ** 2\n",
    "\n",
    "    # magnetic quadrupole\n",
    "    Mq = np.zeros(Px.shape)\n",
    "    for alpha in range(3):\n",
    "        for beta in range(3):\n",
    "            ra = coords[alpha]\n",
    "            rb = coords[beta]\n",
    "\n",
    "            rxja = crossProd[alpha]\n",
    "            rxjb = crossProd[beta]\n",
    "\n",
    "            Mq_ab = 15 * integrate((ra * rxjb + rb * rxja) * jn(2, kr) / kr**2)\n",
    "            Mq += (1 / 120) * (np.abs(k * Mq_ab) ** 2) / td.C_0**2\n",
    "\n",
    "    # electric field modulus\n",
    "    EModulus = 2 / (td.C_0 * td.EPSILON_0 * eps_medium)\n",
    "\n",
    "    # constants\n",
    "    k = (2 * np.pi * freqs) / td.C_0\n",
    "    const = (k**4) / (6 * np.pi * (eps_medium * td.EPSILON_0**2) * EModulus)\n",
    "\n",
    "    return Ed * const, Md * const / (td.C_0 / np.sqrt(eps_medium)) ** 2, Eq * const, Mq * const"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will get the needed data from the simulation results and call the ME function.\n",
    "Note that using NumPy vectorization allows us to quickly compute the expansion for all frequencies at once, although it may require a large memory allocation for a significant number of frequencies. To alleviate the memory requirements, we will divide the data into frequency slices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:34:04.294345Z",
     "iopub.status.busy": "2025-05-15T10:34:04.294198Z",
     "iopub.status.idle": "2025-05-15T10:36:09.795311Z",
     "shell.execute_reply": "2025-05-15T10:36:09.794815Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slice 1 done\n",
      "slice 2 done\n",
      "slice 3 done\n",
      "slice 4 done\n",
      "slice 5 done\n",
      "slice 6 done\n",
      "slice 7 done\n",
      "slice 8 done\n"
     ]
    }
   ],
   "source": [
    "# transforming frequency to wavelength\n",
    "wls = td.C_0 / freqs\n",
    "\n",
    "# electric field and permittivity data\n",
    "E = sim_data[\"fieldMon\"]\n",
    "Eps = sim_data[\"epsMon\"]\n",
    "\n",
    "eps_xx = Eps.eps_xx\n",
    "eps_yy = Eps.eps_yy\n",
    "eps_zz = Eps.eps_zz\n",
    "\n",
    "Ex = E.Ex\n",
    "Ey = E.Ey\n",
    "Ez = E.Ez\n",
    "\n",
    "eps_xx = eps_xx\n",
    "eps_yy = eps_yy\n",
    "eps_zz = eps_zz\n",
    "\n",
    "# spatial components\n",
    "x, y, z = eps_xx.x, eps_xx.y, eps_xx.z\n",
    "\n",
    "\n",
    "Ed, Md, Eq, Mq = [], [], [], []\n",
    "\n",
    "# calling the function\n",
    "\n",
    "slices = 8\n",
    "\n",
    "init = 0\n",
    "step = int(np.ceil(len(freqs) / slices))\n",
    "for i in range(slices):\n",
    "    end = step * (1 + i)\n",
    "    end = None if end > (len(freqs)) else end\n",
    "\n",
    "    Ed_, Md_, Eq_, Mq_ = ME(\n",
    "        Ex[:, :, :, init:end].values,\n",
    "        Ey[:, :, :, init:end].values,\n",
    "        Ez[:, :, :, init:end].values,\n",
    "        x.values,\n",
    "        y.values,\n",
    "        z.values,\n",
    "        eps_xx[:, :, :, init:end].values,\n",
    "        eps_yy[:, :, :, init:end].values,\n",
    "        eps_zz[:, :, :, init:end].values,\n",
    "        freqs[init:end],\n",
    "    )\n",
    "\n",
    "    Ed = np.append(Ed, Ed_)\n",
    "    Md = np.append(Md, Md_)\n",
    "    Eq = np.append(Eq, Eq_)\n",
    "    Mq = np.append(Mq, Mq_)\n",
    "\n",
    "    init = end\n",
    "\n",
    "    print(f\"slice {i + 1} done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will load analytical data calculated using the open-source library [PyMieScatt](https://pymiescatt.readthedocs.io/en/latest/), and compare them with the simulated results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the results, we can see excellent agreement with the analytical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:36:09.796573Z",
     "iopub.status.busy": "2025-05-15T10:36:09.796487Z",
     "iopub.status.idle": "2025-05-15T10:36:09.867595Z",
     "shell.execute_reply": "2025-05-15T10:36:09.867231Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "de = np.loadtxt(\"misc/mie_electric_dipole\")\n",
    "dm = np.loadtxt(\"misc/mie_magnetic_dipole\")\n",
    "qe = np.loadtxt(\"misc/mie_electric_quadrupole\")\n",
    "qm = np.loadtxt(\"misc/mie_magnetic_quadrupole\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "geometric_cross_section = np.pi * radius**2\n",
    "\n",
    "ax.plot(\n",
    "    wls,\n",
    "    np.array(de) * geometric_cross_section,\n",
    "    \"o\",\n",
    "    alpha=0.2,\n",
    "    color=\"red\",\n",
    "    label=\"E dipole - analytic\",\n",
    ")\n",
    "\n",
    "ax.plot(wls, Ed, \"-\", label=\"E dipole - tidy3d\", color=\"red\")\n",
    "\n",
    "ax.plot(\n",
    "    wls,\n",
    "    np.array(dm) * geometric_cross_section,\n",
    "    \"o\",\n",
    "    alpha=0.2,\n",
    "    color=\"navy\",\n",
    "    label=\"M dipole - analytic\",\n",
    ")\n",
    "\n",
    "ax.plot(wls, Md, \"-\", label=\"M dipole - tidy3d\", color=\"navy\")\n",
    "\n",
    "ax.plot(\n",
    "    wls,\n",
    "    np.array(qe) * geometric_cross_section,\n",
    "    \"o\",\n",
    "    alpha=0.2,\n",
    "    color=\"purple\",\n",
    "    label=\"E quadrupole - analytic\",\n",
    ")\n",
    "\n",
    "ax.plot(wls, Eq, \"-\", label=\"E quadrupole - tidy3d\", color=\"purple\")\n",
    "\n",
    "ax.plot(\n",
    "    wls,\n",
    "    np.array(qm) * geometric_cross_section,\n",
    "    \"o\",\n",
    "    alpha=0.2,\n",
    "    color=\"black\",\n",
    "    label=\"M quadrupole - analytic\",\n",
    ")\n",
    "\n",
    "ax.plot(wls, Mq, \"-\", label=\"M quadrupole - tidy3d\", color=\"black\")\n",
    "\n",
    "ax.set_xlim(0.55, 1.4)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error analysis\n",
    "\n",
    "Since the results are sensitive to the discretization, we show the mean error of the multipole expansion by comparing the analytical results for different values of the `resolution` parameter.We observe an exponential improvement that reaches a plateau at a resolution value of 60.\n",
    "\n",
    "The color map shows the Flexcredit cost for each simulation, indicating that an accurate simulation can be run with approximately 0.15 Flexcredits. Due to memory restrictions, these tests were performed with only 81 frequency points.\n",
    "\n",
    "<img src=\"img/errorAnalysisME.png\" width=\"600\" alt=\"Error analysis\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the fields\n",
    "\n",
    "We can now rerun the simulation with a planar monitor to record the field profile for each multipole component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:36:09.869011Z",
     "iopub.status.busy": "2025-05-15T10:36:09.868893Z",
     "iopub.status.idle": "2025-05-15T10:37:23.860934Z",
     "shell.execute_reply": "2025-05-15T10:37:23.860306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:23:38 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'ME_Si_Sphere_fields'</span> with task_id                    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-251d43b8-bea5-4475-9279-a107fb863f35'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:23:38 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'ME_Si_Sphere_fields'\u001b[0m with task_id                    \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-251d43b8-bea5-4475-9279-a107fb863f35'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">5-4475-9279-a107fb863f35'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=195611;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=294863;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=195611;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=709379;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=195611;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[32m-251d43b8-bea\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=195611;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[32m5-4475-9279-a107fb863f35'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-b3152221-3aed-432b-9442-3f044d9b648e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'MultipoleExpansion'</span></a>.                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=922536;https://tidy3d.simulation.cloud/folders/folder-b3152221-3aed-432b-9442-3f044d9b648e\u001b\\\u001b[32m'MultipoleExpansion'\u001b[0m\u001b]8;;\u001b\\.                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "378f237f151a40a192bcab5d5ed5a634",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:23:40 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.081</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:23:40 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.081\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:23:41 -03 </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:23:41 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "f83d587f894e4ff79257d0634f9a1e26",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:23:59 -03 </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:23:59 -03\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:24:00 -03 </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:24:00 -03\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "843e03baa9ec402691e1a00586ad29d9",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:24:25 -03 </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">60</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:24:25 -03\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m60\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4531385b7a1f4e6290c89a89d9e910ae",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:24:28 -03 </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:24:28 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:24:30 -03 </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">5-4475-9279-a107fb863f35'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:24:30 -03\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=56169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=301154;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=56169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=170440;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=56169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[4;34m-251d43b8-bea\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=56169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-251d43b8-bea5-4475-9279-a107fb863f35\u001b\\\u001b[4;34m5-4475-9279-a107fb863f35'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:24:34 -03 </span>loading simulation from ME_fields.hdf5                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:24:34 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from ME_fields.hdf5                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# frequency value for each multipole component\n",
    "ed_freq = td.C_0 / wls[np.argmax(Ed)]\n",
    "md_freq = td.C_0 / wls[np.argmax(Md)]\n",
    "eq_freq = td.C_0 / wls[np.argmax(Eq)]\n",
    "mq_freq = td.C_0 / wls[np.argmax(Mq)]\n",
    "\n",
    "xyMonitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0),\n",
    "    size=(2 * radius, 2 * radius, 0),\n",
    "    freqs=[ed_freq, md_freq, eq_freq, mq_freq],\n",
    "    name=\"xyFieldMon\",\n",
    ")\n",
    "\n",
    "\n",
    "sim2 = sim.updated_copy(\n",
    "    monitors=[\n",
    "        xyMonitor,\n",
    "    ]\n",
    ")\n",
    "\n",
    "sim_data2 = web.run(\n",
    "    simulation=sim2,\n",
    "    task_name=\"ME_Si_Sphere_fields\",\n",
    "    path=\"ME_fields.hdf5\",\n",
    "    folder_name=\"MultipoleExpansion\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:37:24.648847Z",
     "iopub.status.busy": "2025-05-15T10:37:24.648715Z",
     "iopub.status.idle": "2025-05-15T10:37:25.209303Z",
     "shell.execute_reply": "2025-05-15T10:37:25.208877Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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y9JhBXzxCZBYR8swiQpemh0VMno6INB0AwmqIOKe/PyUAiCry4yMiID9eyPMBgKghz8swwtL0mCG/Dz3F9N5ryPcZxLMVIirPiHjmIlllg/waSStoUgQAA52dnfB6vWnmYS5CCJx51klobmpHSWkRNrz9bx5dOgwewRkgB6elFKTza0AaK72Zp3YOdTyZTld6ah+dThhjRHpS44owGlTSUJOna4RRQqUDgEWRG14W4hwLUVYrcX/WJB2NlTRkUks308Axa9Y12eeSWQaOQvxIUem95ZLvE4bcwDGIH0id+BjRk7TvmCK/hkZdQ5GnqyDaBQjDAHT7Eym241T7iaTngLBQyWeYRuUU8nMG+zWfTcsTfvvb32LLf3Zj0z+fxinTbsArr7yCOXPmZLpYWQWP4AwQn893wHLXkKzBpWqU9J5DGAEpj9QQBgMxugIkG6mRGwBWTT7yYVHloys2YjQGAKzEiAw1UuMQbmm6U8iv4SBGdgDASTwrh0qka/LO2k6kOyz0+7YRvwl2ohqQRolpwy5H+hoeOMNRJsrwCVMf7QAixG9qKCbPLKzLTwhR6YZ8lAEAgsQIRIgYqQkq8hGZkOKXplMjOwAQJfZFiFGfmEGMEunysurESFDvPmIEhxzZIUZqUhzZAQBBjeBQhu4R678AoKOrqwvFxcVHOHboiUajOP748bjllq/gxpsuxLPP/B4PPvg8Pv54O+x2etS80ODxLIZhGIbJIX72s9uhKMD1X5sFALh27hfh8Tjx4ycbMlyy7IINHIZhGIbJEbq7u3Hffc9h6dIbYLH0DvtqmoalD92IBx94Hh0dHRkuYfbAa3AYpgBRFEC1qFBUFYqqQNF61xco6oFNUQDlwJTT4bNO4sCQvuhd2yKMA5sQgAEYuoAwjAP/5RlwhjGTh37wDRwzaRRmX3h6Qnp9/Sn43OeOwYPfvxmPPvJShkqXXbCBwzB5hGpRoVm1A1vvv1XtQJpFhaqpUC1qP6PFLDOk3woc0WvwGLoBParDiBm9/47o0GM69GjfRi08ZRimjz179mD5D1/BHxuX9VvwrCgKfvDwTTjzjIX45sKdGD16dIZKmT2wgcMwOYZqUWGxW2CzW6HZNFjsFlhsFmhWDUlELZlBAVSLAtWiwUKtogYAAehRHbGIjlg4hlgkhuiB/+qRJKuHGaaAWLz4BnzpSzNw6qnHSfdPmTIBX/nK2bjzzq/hhRfeHObSZR9s4DBMlqIogMVhhdNlhcVugdVhhdVhgXJAuZU9glUTUADNpkGzabB7epV9faNKwhCIhWOIhqKIhWIIBaOIhqIwKDkVw+QhH374IV588Y/44F/PJD3uvvuvx+Tj5mLjxo2YOnXqMJUuO2GZ+AA5XCaeso8aSgoODLkcnJKC9+4bWjm4TfGQ13ZAvs9JyMFdQn5tF+HTxkVIvgHAaZEPdbiIdCeh1bYRr5VwXQMAIJTG0BwW2Fw22FxW2J02WOzJvz+o5S3JfvcpZ3hmLZVJdt+UI0NKBp8sL6B3xCcSiCASiiISiCIWiJB+eAg1P3nf1KBRkHi4gRg9xRYk9gUIaXlAyKXXAUUu4Q4S8nEACKFHmh4RVLo58nGAlpAPuXwcSOIcMF35eOZl4uef/3kcM2kUli37xhGPXbToZ/jHPz7B/zVuzCrfPcMNj+AwTAZQVAV2tw02l633v04rRAF3ROmgWTU4vU44vb3GtSIEoqEYIoEIwv4IwoEIjCSGB8PkCo2NjXj77X/juV8sGtDxd9xxFY6ZeDV+//vf44ILLhji0mUvbOAwzDCgKIDNbYPDY4fFaYfN2X90jodSB4eiKLA5rbA5rfCU944CRsMxhP1hxAIRhP1hntZicg7DMPCd79yMOxZdjfLygYWJ8Ho9uOvuubj99m/gi1/cAoulMH/qs21JIsPkDRa7BZ4KNyrGlqF2cjUqxpTDU+GRGjfM0GC1W+Apc6Ps6FLUHFeNEePKUVzpgc3F74DJDZ5//i60t/uwcOGlKZ13440XIhyOYuXKgY365COFadYxzBBh99jgLHbAVeyAZk2y7orJCL1rnGwoqiyCETMQ7A4j6Ash1BNmnz1M1hEKhXD33U/jwQe/BoeDXkspw2az4sEHF+DWW5/A1VffA7dbvrYxn+ERHIYZBIoCOIsdKDuqBLXHVaFiTDncZW42bnIA1aLCWeJE2dGlqD2uCuWjS+EqdUIlQ64zzPCy/PFbMGKEF1dceV5a51/65bNw9OgqPPrYN1M67y9/+Qtmz56N2tpaKIqCNWvWHPGcdevW4XOf+xzsdjsmTJiAZ599Nq0ymwmP4KQLuSCUsBnTiLprllqKUkoBqaul7Kpc+WQn1FIOQauoXMQ+FxE800Pcn5uYX3YTiigAcBLBMKnftkNFFooCOIrscJU4YfM4oByQ+0QA9Ik0IknWegQJGVWEiGwdNeTKjxgVhZuM1kzv0yl1SYpoSdSCGtE2qHQL0casqvwatiQR3J2EjMoWf+EKFJcDLpcDriqBqD+CQFcQga5QwsiOi6g3do2+bypQqy0mT7cS6Zohv4aWpBunoomrxDNURBYa5kR1piK+A4Cg+mFKZkdEfCePHwba2trw0NIX8Mqr90FNUreToSgKHn74Jsy64Lu46calqKqqGtB5fr8fJ510Er761a/i0kuPPDW2fft2zJo1CzfddBNeeOEFNDY24mtf+xpqampQX1+fVtnNgA0chhkgdrcN7lInnMUOqAd+tHjNah6iKHAU2eEosqN0pECoOwx/ZxAhHy2JZhiz+cHD38SMGcfjC1/43KDyOf30E1BXNw3fX7oQjy9/ZUDnnH/++Tj//PMHfI0VK1Zg7NixeOyxxwAAxx13HN566y388Ic/ZAOHYbIVzabBU+qC0+uEhXJ6w+QtiqLAWeyAs9gBQzcQ7ArC3xFEJJjEBwvDmMDuz4CzzznJlLzOPuckrPvTZ/D5fAnpdrsddjs9wj9Q1q9fj7q6uoS0+vp63HrrrYPOezDwGhyGOQxFAVxeB0aMLUPNMZUoGuFh44aBqqlwl7lROb4CVRMr4Cl38XodZmgRAjAMEzaBzZs3w+v1JmxLly41pZhNTU39pr+qqqrg8/kQDMqdRw4HPILDMAew2DQUl7vgLnXFp6AYRobVbkVJjRfe6mIEu4Loag3wqA6T1UyaNAkbNmxISDNj9CabYQOHKXgcRXYUlbvhKLLzkCaTEoqiwFXigqPEhUgwip5WP/ydmftiZfIM48AIzmARAqqqDlmYierqajQ3NyekNTc3o7i4GE6nXDQyHLCBkzJ9cajkP4UKoVpQCEVU7z650zHqHEpdlWpcKcA8tZRTyBuOm4grBQAeIraUW5M/jyJKLWWlFFH09IEuBNylLhRVuGE9EPNJNwQChC8Uf1TeyQSJeEIhIY+rAwAhVf4DGIY8ParIF7fGFPmIgSDi8ACAQUhSBCVVEUQ6ofxTkpiIKtlm5O/VAnk9sAp5fbbrdEfqiMr3ORS5Ms9JxDFzW+X3YFcVaHYLvCO98FQVoafdj+5WPwxdwEFMY1kJZYyV6A+0mDwfi57smVOx8Si1J5nRkCOIuiYIhZOSRC2Y+rXl6coB/+IZ0xII8wycoWTGjBl44403EtLefPNNzJgxY0iveyTYwGEKClVTUFThhrPUBS2JjJxh0kWzqPBWFqF4hAf+jiD8rX7EqOidDHMkTDFOUsujp6cHW7dujf+9fft2bNq0CWVlZTj66KOxaNEi7NmzB7/4xS8AADfddBN+/OMf4/bbb8dXv/pV/N///R9efvllvP766yaUPX3YwGEKAs2qoqjCA0+ZC4qqwMigfwumMFAUBZ4yF4rL3Qh0BeHb34NoiB5dY5hs4d1338W5554b/7uhoQEAMG/ePDz77LPYt28fdu3aFd8/duxYvP7667jtttvw+OOP46ijjsLPf/7zjErEATZwmDzHYtNQPMIDd6kziXNGhhlaXF4nXF4nQt0hdDX38IJkZmAIw5wpqhTDkJxzzjkQST4CZV6KzznnHLz//vuplmxIYQOHyUssNg2lVUVwl7Bhw2QPjiIHHEUOhLrDaG/ysaHDJCdH1uBkK2zgMHmFZu1d/+AudUJL0705www1jiI7qj0VCHaH0NnUzVNXjBw2cAYFGzgpowBKn5JKBvWjSjuKo2NOUWopufLDQsWiUmhfBzZFri5JVS3lMYrk6UT+AFBkIdRSRKBKqyp/5mG9d/Gwt9KD4go3oCiICqAnycJOf0z+5ewn1E89ao80PaT4pOlh+Mlrxwy5Kko3wtJ0Q8jvg1Kd5At0jDYiJlOSmGsWhVBeKXKVn4Oq5yF5u3ATaiwAcBP13K6p0NwOlI93wN8RRGdzN2IRHQ4qThrVH0TpEUpFJ1RUBtFPUd0XFaNNSRIPitgnVEItRdVzMhhVkphrZNtITZEl+kZ/C9M+yHnYwGFyGkUBike44a30sHM+JmdxlzrhLnGguy2AnpZuGBzkjAF4BGeQsIHD5CyuEgdKq4sBC4dRYPIApdeFgbfcic7mHnS30qOATIFgoqO/QoQNHCbnsDmtKKsths3VOy3AH7tMPqGoKkprilFU7kLHPh+CPvnUJZP/KBBQTJiKNiOPXIQNHCZnUDUFpTXFcJfKPSAzTD5hsVkwYnQZQj1htO/tQjTIC5EZJhWyctHCk08+iTFjxsDhcGD69Ol45513yGP//e9/48tf/jLGjBkDRVGwfPnyfsfcc889UBQlYTv22GOH8A4Ysykqd2HksZVs3DAFh8NjR+3EESipLoLCLg8KC7OiifMUVXawatUqNDQ0YMWKFZg+fTqWL1+O+vp6bN68GZWVlf2ODwQCGDduHC677DLcdtttZL7/9V//hT/+8Y/xvy1EXKMjo6JXSUXF1ZErJiilVO85qaql5GoRMhYVEW8KAGyEWsohCLUIEVuKUksVEXGlAMA9gLUzNqcV5Ud5YXNa4Y8ZgN5fadGjy79sfZArnwAgoHZL04NGpzQ9bMjXQ+g6oYgSqfs3oeIDkaohTf6+VaIO9l5Dnhd1jVShFF8AICDfZxDPyiDifOmG/PgYoUIDgDDkarcQ8ax6iPht3YTqyqmWkNd2xeQKw+KYvI15NHl/4K30wFPiRPteHwK+g/VuIO2oH8RrIuNBESojI4mKyqDUUpRiKcVrJ1MRqkT8KuoMOhabiB+REYRI2UmfPJ/BZ5GLZN0IzrJly7BgwQLMnz8fkydPxooVK+ByubBy5Urp8aeccgoeeeQRXHHFFUlDv1ssFlRXV8e3ioqKoboFxgQUpXc6qmZiBWxO+gebYQoJzaZhxJhSjBhdyrHUGOYIZFULiUQi2LhxI+rq6uJpqqqirq4O69evH1TeW7ZsQW1tLcaNG4err746IY4Gk104PHaMnDQCxSPoSOQMU8i4vA6MnFQJTxlP2eY1PEU1KLJqiqq1tRW6rqOqqiohvaqqCp988kna+U6fPh3PPvssJk2ahH379uHee+/FmWeeiQ8//BBFRfLh43A4jHD44JC3zycf4mbMQ1EVlNUWo7icO22GORKKpqD8KC9cXidaPuuEHuWI5XmHaX5wWEWVt5x//vnxf5944omYPn06Ro8ejZdffhnXX3+99JylS5fi3nvvHa4iFjwOjx0VR3lhsbFPG4ZJBYfHhqMmjUD7Ph+62wKZLg5jJkKYY5wU6AhOVk1RVVRUQNM0NDc3J6Q3NzejurratOuUlJTgmGOOwdatW8ljFi1ahK6urvj22WefmXZ95iCKoqB8pBfV48rYuGGYNFHU3nZUNbaM1+YwzAGyqiXYbDZMnToVjY2N8TTDMNDY2IgZM2aYdp2enh5s27YNNTU15DF2ux3FxcUJG2MuNpcVtZNGoIinpBjGFJxFdtQeMwIur1wFxuQYvAZnUGTdFFVDQwPmzZuHadOm4dRTT8Xy5cvh9/sxf/58AMDcuXMxcuRILF26FEDvwuSPPvoo/u89e/Zg06ZN8Hg8mDBhAgDg29/+NmbPno3Ro0dj7969WLJkCTRNw5VXXply+RT0BdqkZOJUOv2oyWCbhISVloPLpdpUQE0AcEAuVXURMnGPkBsjVOBMOxHR21vpQVGlB4YChPXEIVg/Ifvuhnz43ae2S9MDRoc0HQCiMbnsO6oHpemUjFQlmhAl5e/dJ3+GNlW+qJp6fzbiXdgEHfjRIgiXBIKQjxNRFg1Cd6pT+mMAMUX+XiOqPMBpxCJ/3xEhf0cRQsoPADFDnhcpOY91ya8BuXuBkCY/HgB6iPfao5ZK04v1Mml6kS5/3+7DZeUK4D3KC8Vphb6vG0L64yZvryImvwb1vpPKxBX5sxVqasFjdSGvNypRnwBAEDJxMqimIPrtQ47PiIkgwDLxQZB1Bs6cOXPQ0tKCxYsXo6mpCVOmTMHatWvjC4937doF9ZAfzb179+Lkk0+O//3oo4/i0Ucfxdlnn41169YBAHbv3o0rr7wSbW1tGDFiBM444wy8/fbbGDFixLDeGwNoFhUjji6F02NDrEC/KhhmOCgud8FTZEfLrk5Egqn7ZWKyAF5kPCiyzsABgIULF2LhwoXSfX1GSx9jxowhvlAO8tJLL5lVNGYQOD12jBhdAo2jfjPMsGCzW1A7oQJte7t4ATJTcGSlgcPkHyVVRSitkk97MQwzdCgKUDHSC6fHjpbPOiHMmPJghgdhcDTxQcAGDjOkqJqKytG9U1IMw2QOt9cBq6MC+3d2IByQr3tisgwhoJhh4BSoUctzBcyQYXdZMfKYCjZuGCZL6JuycrPKKjcQOOALZ5BbgcIjOKmiqICikEE1yYCJahIVFbHPQgTbpFRUFkXeadkVubdmAHAacoWHS8iVOy6VCCZ6mNqmqMyFiqO8CBk6ECOCYRJqmG61U5oeEHJVVIhQvFCKKIAOCqkR79WmyZ+TQ/NK052QpwOAW8hdDrgN+TN3EsEXHcRaJmuSNU42YpdKRKmmgldTfaaRpDONEB+iUV2+I0R8uQaJIJx+hX7ffovcE3kQ8roT0lOrU+EYHdg1SpQrqslVX2EiEKxfkauuimIl5LWLCQVezZgydO73o31f4nOh2neMqJs6aCWTrhLBUolzqOMtVDDWJAFtyQCdhEKT6s8P5iMKVomUy7CBw5hOeW0xvBxHimGympJKN2xOC/bv6IBRoFMYWQ+rqAYFGziMaaiaiqoxvN6GYXIFV5EdI48Zgabt7YiG6dEYJkOYZuAUpgHLa3AYU7DYNIycyOttGCbXsNp7266D2y6TZ/AIDjNoHG4bRo4r5xg4DJOjqJqCmnHlELs60N3O/nKyBiHMUUAV5gwVGzjM4PCUOFF5NDvvY5hcR1GAylElsNo0tDfJFzozwwxPUQ0KNnBSRgWgpBxzilJXAXTMKSrdohCxqEDFLEoSi4rY50wSO6uPkkoPymt6FUFdMblfjS5Vrl4BgB6lVZoeMDql6VT8KJ1QUyiUBAiA3SJ3OujU5EoVD8ql6V6jRJpepNGxqDxE1HQPMQLmJl6FnQi+7kgSlN2qyjs6jZCIUE+Q6i7pyERA1JDnFtLlBQ4T6f6YvF30xOhn3hOVKwm79QppepfWKc9Ha5OmB3U67llEJ9RShPIqpoTlx1so1RWt4ApBfn/emFzJ51KtKK0qgsWqYf9nnfF0qj+IJulbIkLeJ0QRkqbrROwqnXgeVP8IAAah1KL75yPFqMqQgcCLjAcFf3YzaVEx0hs3bhiGyS+KylyoGVee9COByX+efPJJjBkzBg6HA9OnT8c777xDHhuNRnHfffdh/PjxcDgcOOmkk7B27dphLG1/2MBhUqby6FJ4K1gGzjD5jKvIjtoJ5VB5+jlzmOHkL83pqVWrVqGhoQFLlizBe++9h5NOOgn19fXYv3+/9Pi77roL//u//4snnngCH330EW666SZccskleP/99wfzBAYF11xmwCiKguqxZSgqpYelGYbJHxwuG0ZOrIDFmmTekxk6+qaoBrulYeQsW7YMCxYswPz58zF58mSsWLECLpcLK1eulB7//PPP484778QFF1yAcePG4eabb8YFF1yAxx57bLBPIW3YwGEGhKoqqB1fDncxu3hnmELCZrfgqIkVsBBrx5ghRKBXRWXCZhgGfD5fwhYOy9c3RSIRbNy4EXV1dfE0VVVRV1eH9evXS88Jh8NwOBJ/H5xOJ9566y3THkeqsIHDHBFVUzFyQgWcbvaTwTCFiMWq4aiJI2C1sy4lV9m8eTO8Xm/CtnTpUumxra2t0HUdVVVVCelVVVVoamqSnlNfX49ly5Zhy5YtMAwDb775JlavXo19+/aZfi8DhWtriiiKCkVRSFUUpa5KFosq9ZhT8nQ7XNJ0p5CnA4AT8mv3rblXNRW1EypgdVhgAOgy5HF1utR2aXqPkKtOACBMxPuJ6IQfDkIJQD0np6WEvLZHq5Sml+hytVSpJn+GXqf8q9ZroxdnFhPijyKLfBjZbZFrk1ya/HiHRmuZbCrxDBVCZZGijEoX9DdTxJDvo1RUAV1+cX9Mfnx3jL62Lyrf1xWRt8viiHykskOXL6zvtNAL7nsU+ZqFYKxTmq4b8q9qPUrEZNLoyOBRTZ5XVJUrmbxGmTTdqdigWlTUTqzAnq2tiIR6VUpU/wEAUaLfoVRiZDrVDyaJRUXFqTIEoa4i6u3B/lxkRmltokx80qRJ2LBhQ0Ky3U4rD1Pl8ccfx4IFC3DsscdCURSMHz8e8+fPJ6e0hgMewWFIVE3FURMqYHOwHcwwDKAdGM3lPmGYMHENjqqqKC4uTtgoA6eiogKapqG5uTkhvbm5GdXV1dJzRowYgTVr1sDv92Pnzp345JNP4PF4MG7cONMfy0BhA4eRwsYNwzAy2MgZRoQ5629SHX6y2WyYOnUqGhsb42mGYaCxsREzZsxIeq7D4cDIkSMRi8Xw61//GhdddFFat24GbOAw/WDjhmGYZLCRk/80NDTgqaeewnPPPYePP/4YN998M/x+P+bPnw8AmDt3LhYtWhQ/fsOGDVi9ejU+/fRT/PWvf8XMmTNhGAZuv/32TN0Cr8FhElFVBUeN546LYZjkaJqKoyaMwGdbWjgS+VAhhDleiNNYQDRnzhy0tLRg8eLFaGpqwpQpU7B27dr4wuNdu3ZBVQ+OkYRCIdx111349NNP4fF4cMEFF+D5559HSUnJ4MufJvwrxsRRFAUjx1fA7qRdoDMMw/ShHRjt/ew/LYhFkwXqYNKiTyZuRj5psHDhQixcuFC6b926dQl/n3322fjoo4/Su9AQwQZOymhIFotKJWKdJIubQu3TiLwskC8MsxGxqOyCVjrEDuilFEXByHFlsLos0GHAD7mioYuIxePX5enhmFwpBQAxQpFFKdSshJLJZSHiRCk15LXLiRhSZXb5syq1y8tURjxar5X+6iqxyX8IvFa58sNtkac7bfJ0u43+mrYS5VI1c2LVGDo96x0llExhQskUjMjbBRWLqitKt7HOiFx51WWVl6ndKi+TK0zEMIvQbaxNlbfLLqv8nEBM3paihLowHKPjvekGEd+JqFMxTZ7uNeRtzAE7FIuC6gll+GxLC/TYwXpE9TthRf48wpDfH9UP6mn0qaoibxuCSIfoqzeFGawy1+E1OAwAoGZ0GVwe8ySDDMMUDjabBUeNr4CqcuwqUxEmKKjS9GScD7CBw6BqVAk8XvZQzDBM+tgdVowcV8EBOs3ELE/GbOAwhUh5dTG8ZRw4k2GYweN021AzWu4skEmDDMaiygfYwClgvGVulFcVZboYDMPkER6vA5VHlWS6GAzDi4wLFXexA1WjSjNdDIZh8pCScjfCkQja93dnuii5TZ+jPzPyKUDYwClA7E4rasfIVREMwzBmUFHjRSQcQ0+XXC3JDIAM+sHJB9jASRFF6ZOIpxZsk0oHaGm5RZEv/LVS6QYVOPNg5dYsKmrHlQMKICDQo/ZIz+mGPHhmQJenU1LVmC4P6gcAqiKX7toscimuyzJCml4m5HLwEfCS1y4jHBmW2+ULJCsc8g6inJB8l9roAIglDrkE30OkOzxyCavVTQTOdCZZ5OmQ71MsRL0lqi3V54pYks44JH9WelD+rKJ+Ijhnj/zdlYRoFaCX2NdByLtdFnnddKjyMtmIgJAAYAvLPyYsRPerEBL1gNIiTY/E5G0YoKXlAvL3JCB/R4Yqr4NFCr3exmE4UTW6FJGtMYQCB9+xlZCPW1V5vxaFPJ0Kzgmk0w8f6Xhz3CikTIb94OQ6vAangFAUBSPHVsBCdN4MwzBmoioKRo4th8XKfQ4z/LCBU0BUH10Kp4t2SMYwDGM2FouGkWPLWT6eDhkKtpkvsIFTIJRVFqG4RO4JmGEYZihxOG2oOZrl4ynDMvFBwQZOAeAqsmNEDb0ehWEYZqgpKnGirJLdUjDDBy8yznOsNgsrphiGyQpG1HjhC/XA302LD5hDMG2RcWGO4LCBkyIK1N4txVX6VMC43n1EsE3i9VjFwGJGKYqCkWMqELbQnUkPWqXpAaNTmk4Fz9R1uRIm2by7WWqpSlU+OlXhoIPxjSAiU1TY5GqJCrtcRVJOKJ9KXbQ01l0sf1b2UiIQppcI4OqR34TiThINnlDogFoESqiGYBCqkmQRpaPyZ6j55QEeLT3Ec+qSpzs66CCjTp/8Go6APPAjpYqyEu3YqtGD4Zoqfx9KiBhVpQQ7afTWqaobSXWVRf4DKVT6fRtqhTS9dnQ5tm9u6hd9nOrXqH6Q6jd791EBOlNUv4oMT3KY5QcnQyKwTMNTVHlM9VGlcDiT/NgxDMMMM5qmYuQYufHDHAavwRkUbODkKd5SN8eYYhgmK3G6bKgayZ7UmaGFDZw8xO6wouoo7jwYhsleSis88BTLpwiZAwhhzlagnv7YwMkzFEVB7ehyqCr7nGAYJrupPZqdACalb5Ex+8FJCzZw8ozKkSWwJ1lcyzAMky2omsLrcZJhlqO/Al1kzCqqFFEUNb5J96cYoyrZPhWE+oK4htULuCssiCBRIRFAJ3ntVNVSsZhcfUHdg1Wj1wE5Nfk0WqmolqaPUOSqk3K7vBpXJBGbUWqpKrtcbVPhlN93WbE81o+rVJ4PAFgqCKVdmdwRo0IN43uIdHeSYX874cmaUlelGoyKUEoBAMKE0s4vV5xpPfJ0tVierrhptaClVb7P2iFXAVkUIs4XERcJRFsFAEEocYSQP3NBqKsE8RUuNPqZG0J+f1T8Kqp9kyT7BSGqjkspSfhbcwNFNTaEm+TXJvvBNPrUVPvnZNdgsp+sfHtPPvkkxowZA4fDgenTp+Odd94hj/33v/+NL3/5yxgzZgwURcHy5csHnWcuYrFoqBnF/m4Yhsk9RlR54XQPzP1FQcEqqkGRdQbOqlWr0NDQgCVLluC9997DSSedhPr6euzfv196fCAQwLhx4/DQQw+hulr+5Z9qnrlI7dHlsBARoRmGYbKdkUdX8NrBw+E1OIMi634Rly1bhgULFmD+/PmYPHkyVqxYAZfLhZUrV0qPP+WUU/DII4/giiuugN0u/wJINc9co7S8CO4iViMwDJO7WG0WVNVyvKoETAu2mekbyQxZZeBEIhFs3LgRdXV18TRVVVFXV4f169cPa57hcBg+ny9hy0Z6OwWWhDMMk/uUlHvgLqLWOTFMamSVgdPa2gpd11FVVZWQXlVVhaampmHNc+nSpfB6vfFt1KhRaV1/qKkdVQ6Fh3UZhskTakfxVFUcnqIaFKyiIli0aBEaGhrif/t8vgQjh16NL/fpoID29aARKgEq1kpM9MY/Ki0vgt2jQkevOiUMuTKCUkoBqaulKCya/KvLoRWT5xQp8phTJUIecbjEJq+upTZ5Z1hmoxs1HVtKft+lRXLljqtcrpayjkjyvsvlaimUyWNzwUukF8nThYuerhR24uuYUlGlHIuKVvQoYUKBFyDidnXL67PikKdbLPQzVzRC7abK358Q8jJRvxM66B9kXRDt2JCfEzPk7yISlreLKNGOAEDX5Mo1Q8jfUzQmf04pq6sA+teFqFJ29NZnxQpU1Hqwd3dvnDwyVh/xXAG6v6X756z61j+IEBAcbDNtssrAqaiogKZpaG5uTkhvbm4mFxAPVZ52u51c05MNWK0aqnm+mmGYPKSk3IOuLj/83XTQWoY5ElllttpsNkydOhWNjY3xNMMw0NjYiBkzZmRNntlAzVEVPDXFMEzeUnNUORSlwPs4AZPCNWT6RjJDVhk4ANDQ0ICnnnoKzz33HD7++GPcfPPN8Pv9mD9/PgBg7ty5WLRoUfz4SCSCTZs2YdOmTYhEItizZw82bdqErVu3DjjPXMNb4uYYLgzD5DU2mwUjqksyXYzMYpqKKj0LJ1X/ccuXL8ekSZPgdDoxatQo3HbbbQiF0pjeNImsmqICgDlz5qClpQWLFy9GU1MTpkyZgrVr18YXCe/atQvqIesC9u7di5NPPjn+96OPPopHH30UZ599NtatWzegPHMJVVVQPZId+jEMk/+Uj/CirbMD4RDtGTyv6TNwBp1P6qf0+Y9bsWIFpk+fjuXLl6O+vh6bN29GZWVlv+NffPFF3HHHHVi5ciVOO+00/Oc//8F1110HRVGwbNmywd9DGmSdgQMACxcuxMKFC6X7+oyWPsaMGUO6MB9onrlEVU0ZNHboxzBMAaAowMhRI/Dplr2ZLkrBcaj/OABYsWIFXn/9daxcuRJ33HFHv+P//ve/4/TTT8dVV10FoPe3+corr8SGDRuGtdyHkpUGTnajItnMXjoxTaiV/eKwCGkOpw2lFUUIC7mKJGjIFVGRWDd5bVodITcaNVUey8hGxJxyavRC6CJdHnOnyCpXRxQRaqliIryS1yqPwwMAXptcXVLsCkvTncXy4y1lRByx0iRTiJRaqkyuOBPFRHqRXFUDN6HSAgBtiCM3J7ltocvLJexy5Y5ikXdPipa6ga8SX8EWXX5tZ4SoHzH5tcMG/VxDuvycEKGWChHqqqAubxehqLwdAUBYky/S7VNiHo5hyEdKdEP+PJKpq0ilqYVYV0O8VofLjpIyNzraE/sxqt/s3WdWbKkMf0z2ycQHnY+AYRj9/LlRYpo+/3GHLgc5kv+40047Db/85S/xzjvv4NRTT8Wnn36KN954A9dee+3gy58mPBSQQ9QexVF3GYYpPKpqyhKWJhQMJnoy3rx5c4JvN6/Xi6VLl0ovm47/uKuuugr33XcfzjjjDFitVowfPx7nnHMO7rzzTtMfy0ApwBqTm5SUeuB0sYdPhmEKD82iobK68Dy2CwEIQ5iyTZo0CV1dXQnboSM0g2XdunX4/ve/j5/85Cd47733sHr1arz++uu4//77TbtGqvAUVQ6gqiqqa3lhMcMwhUv5CC862rsRDsmny5jkqKqKYmK6+3DS8R93991349prr8XXvvY1AMAJJ5wAv9+PG264Ad/73vcyMgLHIzg5QGV1CbQkXloZhmEKgYJzbpohmXg6/uMCgUA/I0Y7sOZvIEKgoYBHcLIcm82Csgp6ESHDMEyh4ClywVPkQk+3fHF43mHaImMgSTQRKQ0NDZg3bx6mTZuGU089FcuXL+/nk27kyJHxdTyzZ8/GsmXLcPLJJ2P69OnYunUr7r77bsyePTtu6Aw3bOBkOVU1ZezNk2EY5gDVtWXYurlQDByz/OCknkeqPunuuusuKIqCu+66C3v27MGIESMwe/ZsPPjgg4Mvf5qwgZMmqcoN0wnmpjl1OL0WREWiFDNs+KTHR2Jy+XhMp6WcAnIpNRWsTlPl8bnsqlyy7Bb0nK9bkeu73YSfHw9RW4ss8sCPHgsd+NFjk8thHU4ieCahyFY9cumukszTdBEh43bLpfakHNxDyM2zNYQH9RVH3AfVJSs6If9PFugzIt+nEg7krEVyGbUjKD/eE6Ed0fmj8oobIOTjAaL++4n0QIzwkwAgSLS/iCrvK6KqXFauE/Jxqv8A6H6H7AuJ9m1XE+9BtQPuUgvC7eSlU+5vU5ePFwap+KSzWCxYsmQJlixZMgwlGxj8VrOY6hqWhTMMwxxOVXWBjGybFYuqQGEDJ0vxFLng9nC8KYZhmMOxWC2oGFGS6WIMPQIQhjlbIcIGTpZSXcOycIZhGIoRlaWF6fyPGTBcO7IQr9cDp1O+1oVhGIYBNE3FiMqSTBdjaMlwNPFchw2cLKSyusB8PTAMw6RBxYhSaGnEJssZ+mTiJoRqKERYRZUF6OKgQsHr9cBqV6GLWEL6oUSJAIExQ66AMIh8kqGpcnWQVZOvC7KpciWMQ6fXETkJ54VOIhifgxDhOFT5BLNDoyee7Ra5+sNik5+j2IngfQ6iCdmSNC27XPUiHMSoHXF81qqlUoW6jxSfk0I9J4B8H9T7U+zyNkPVD6o+AXQ9pOutvK5R7cKZxMeII0a0V03eXq2aPDCvbshVZTGDvm+q36H6KYVQlZGKTsUKKEDZiCI07Wsly5HLCGHS+pkCNXDy2PTNTSqree0NwzDMQCmvKMnvURwmbbhWZBFerwcOR5KvUIZhGCYBVVVQMSJPA3HyGpxBwQZOFjGiitfeMAzDpEpFRUl+KqoEAMOErTDtG16Dky14ilysnGIYhkkDVVNRXuFFy/6OTBfFVHrX4AzeOslUsMtMk4cmb25SWcmjNwzDMOlSMaK0MLwbMwOGR3CyAKsDcLgt/VRTUZ2ILUUoGgyDisVDL8NXiCqgEioqiyIfZbIKIkYV5PkAgI1YGGilQtWkmK4RKhUAUIl9ikZ86RAKFoUaFk+qcEqxEzYK1A1pyved5LkS74N8f0TPSNUPqj4BdD1MtT5T7YJqRwBgjxFqSKK9Uu2b6g8UI0JeW0DeH1H9VEyR92sK9V4PU4IpGlBUYkd7exdZppyjb4pqsBRoF8IjOFnACB69YRiGGTR515cKk7YChUdwMozVaoG3hIgWzTAMwwwYu92GomI3fF2+TBfFHIQwZQ1OoRo5PIKTYcorSukhWIZhGCYlKiryVDLOpAyP4GQQRVFQUVGS6WIwDMPkDUVFbtjtNoTD9PqgnMGsNTg8gsMMN6WlxVDZAyfDMIyp5Ivjv75QDWZshQiP4KRMb00RKdYYQ/RXDpSWF0EIHbog4rwQ6bouT6dUC8lQlCRxXqTpck/LFhDpSWSbKrGPEiCpxGeIYubniSAuTjxaEZPH4lGiSd5FlIgNFpF/cSpEurARXq+TxCbKSnTiGRL3TT0n8rkCAPE+qPdHNiWqfqQBVW+pek63F7pMVPuj2ivVvqn+IEb0HwAgJH0eQPdTVL9GSb8Vg/449Ja4sWePAWOASjy6PzcO+y+TS/DwQYZwuRxwuRyZLgbDMEzeoWkqSkuLM12MwcOejAcFGzgZoqy8JNNFYBiGyVvyoo81aYqqUA0cnqLKAKqaJ18XDMMwWUrfKHkgEMp0UdKHFxkPCh7ByQClpcX5GRiOYRgmiygt82a6CEwG4V/ZDMCNjmEYZugpLfXmdHwqIczbChGeokoTQYwbUqvx9QPxo+x2GxxODfohMVwOj0EVT9cJ9Qzkyg8qYmyyBk4qFBS57aso8iqjCfOUO1RbNAiHiDqhbNGTqCyiMfm+WFielzVMvO+AXBGiOOWKEACAPSg/xyp/toJ6F0RdE04XfW0robxKGjsrBZJ5XY0SKrFgQH68n3hO/tSOBwAECOUh8f4E8b5jYfm7iEbp+k/VQ6reUvXczN8oqr1S7ZvuD5LUG6LAZGRrRd6vUf2gkuT7XFOMvoPgKbahs8N34NpEm8lWlZQwR+JdqAYOj+AMM2VlJZkuAsMwTMGQ030uq6gGBRs4wwxPTzEMwwwfRUVuWCw8WVGIsIEzjHg8LliJaQiGYRhmaCgty03VqlmejHkEhxlySkt59IZhGGa4ydm+lxcZDwo2cIYRr7co00VgGIYpOJxOB2w2ebiJrEYogGHClqaB8+STT2LMmDFwOByYPn063nnnHfLYc845B4qi9NtmzZqV5s0PHjZwhomiIjc0S47FCGIYhskTSkpyc5oqU6xatQoNDQ1YsmQJ3nvvPZx00kmor6/H/v37pcevXr0a+/bti28ffvghNE3DZZddNswlPwgvCEmRIwXZFEIudfQUO2g5uEFIWIm8qHQa2o6lpJaULJRCKPLnkkw1bBDjpjFDLj2NEY8+RBwf0ul7CEbkX3P2kFw2bOkmpKoWIsCjJUmQUWoHERhQCRPBJUOEHNzuJ68NKkCnxaSv21iSgJdUMFHq/lKVg3fR9210yb3ZGl3y8ka75fmEQ/IuMxilu1KqHlL1lqrnVDrVjgDaCS7VXilImXjSb2RqX2r9miDyofpN6tJFXgf2NqXWd/b196kGVzYLYZJMPJ0RnGXLlmHBggWYP38+AGDFihV4/fXXsXLlStxxxx39ji8rK0v4+6WXXoLL5cqogcMjOMOEt4SnpxiGYTKF0+XMyWkqIZTBb1BgGAZ8Pl/CFg7LjcRIJIKNGzeirq4unqaqKurq6rB+/foBlfvpp5/GFVdcAbfbbcpzSIesNHBSmfcDgFdeeQXHHnssHA4HTjjhBLzxxhsJ+6+77rp+84IzZ84cyltIwONhmSLDMEym8RbwNNXmzZvh9XoTtqVLl0qPbW1tha7rqKqqSkivqqpCU1PTEa/1zjvv4MMPP8TXvvY1U8qeLlln4KQ67/f3v/8dV155Ja6//nq8//77uPjii3HxxRfjww8/TDhu5syZCfODv/rVr4bjdgDw4mKGYZhsoMSbWwaOaTJxA5g0aRK6uroStkWLFg1JuZ9++mmccMIJOPXUU4ck/4GSdQbOofN+kydPxooVK+ByubBy5Urp8Y8//jhmzpyJ73znOzjuuONw//3343Of+xx+/OMfJxxnt9tRXV0d30pLS4fjdgAU9lcDwzBMtuD2uKBpOST2MMG4EUavoaSqKoqLixM2u90uvWxFRQU0TUNzc3NCenNzM6qrq5MW2e/346WXXsL1119v2mNIl6wycNKZ91u/fn3C8QBQX1/f7/h169ahsrISkyZNws0334y2tjbzb0CCw2HPyXlfhmGYfCSXRtQFTFh/c2ANTirYbDZMnToVjY2N8TTDMNDY2IgZM2YkPfeVV15BOBzGNddck9Y9m0lWLQxJNu/3ySefSM9pamo64jzhzJkzcemll2Ls2LHYtm0b7rzzTpx//vlYv349ac2Hw+GEBVg+34FgbQd0CYYhV9voSFSEeIqKYRhJlCUADINSD8ivQakQaMz8YiGCEBJliiZRfEV0eV5hQnUSJLKyE8f3xOjqbVXlsgJLkAhYSmVEKOOsSRQeIiS/ETUoz0vxyBVAcMpVQ4qdUEoBgI14JtRXLRVMkVLu6EnqZoSoz5SKKkgEm+0hFFE+Ih8ARrf82tFO+f0FuuTPsCfokKZ3R+lnTtXDQIr1PEykU+0IoNtfjAhsSeuuzCRFJRPR+gxChQYACug+111kR0trYl2h+vO+/j5rg3EOIQ0NDZg3bx6mTZuGU089FcuXL4ff74+rqubOnYuRI0f2W8fz9NNP4+KLL0Z5eXkmip1AVhk4Q8UVV1wR//cJJ5yAE088EePHj8e6detw3nnnSc9ZunQp7r333kFfu7iYp6cYhmGyheIiT6aLMGB61+CkNvoizyj1U+bMmYOWlhYsXrwYTU1NmDJlCtauXRsfUNi1axdUNdFI37x5M9566y38f//f/zf4MptAVhk46cz7VVdXpzxPOG7cOFRUVGDr1q2kgbNo0SI0NDTE//b5fBg1atRAbwVA7/Sa2+1M6RyGYRhm6NA0DW63C37Kz1I2YVKYhXTzWLhwIRYuXCjdt27dun5pkyZNgsiiuBBZtQYnnXm/GTNmJBwPAG+++WbSecLdu3ejra0NNTU15DF2u73fgqxUKSpyQ6GG+BmGYZiMUFycG6M4Zq3BgSjM36GsMnCA3nm/p556Cs899xw+/vhj3Hzzzf3m/Q6Vtt1yyy1Yu3YtHnvsMXzyySe455578O6778atzp6eHnznO9/B22+/jR07dqCxsREXXXQRJkyYgPr6+iG9l6Li3FnMxjAMUygU5dA0FZM+WTVFBaQ+73faaafhxRdfxF133YU777wTEydOxJo1a3D88ccD6B2O/OCDD/Dcc8+hs7MTtbW1+OIXv4j777+flMiZRS7N9TIMwxQKbrcLqqrCIMKjZA0mrcHJolmjYSXrDBwg9Xm/yy67jIx34XQ68Yc//MG0svXOLxoAGXupdzW+1WqF1abBOKBiSBbLRJCxWYa+VlLqAEpVEFPlSpWYKlcNhRVa2RLU5dXPFpMPLFpVebpKTANqaajHDGIoN6bL06OEQsYZpFVUdh8R76pLnq645aoh1SG/tmJPct9W+T7FYs5grqACJgFAlKjnhDzIIOKCCT+h5OuhLx32y+87GJB/5PSE5K4duiLy47uSxKLqisqv7YvK61Q3IQAKEM82mES5RrW/mCKvnzFDfvyRVEZDCdkPkkowgBKWHRpTy+1xoKvLd+Av+QkHY1FlxkIQJq3BKVSy0sDJB4qKMhd/g2EYhklOUZH7EAMnWzmwhmaQmJFHLpJ1a3DyBY+HDRyGYZhsxc19dN7DIzhDhMfD628YhmGyFZfTmfXrcIRI7tAwlXwKER7BGQIsFgvsybzJMgzDMBnH7XZlughJ6VuDY8ZWiLCBMwRke6NhGIZheClBvsNTVCmjo3fhFzWsGYPbbYc4LAZMMrXB4ccmXkt2PBEviXQqmOza8rx0IsZSTMjVFyEhl7BYFDrQqGbI1SVaNDW72xDy4ylFFABEhLzqh4jh4KAuL2tRTP6c3IQKBwCcPfJzHB1ypYrVLq8HFps8H81GK9cUosUrlPCKehVElUoSegyCCEWlRwjlWkR+8WhYPjoaitDdWSAiPydAqOC6o/L35yMUft0xWrlGqaK6iNfUHZU/3B5ChdZDtEkACKryeGVUe6XaN9UfJFcXpTb1Q/drVD+YJDOq6R92kstlgxCxJP1537UzNAQizFlknCSaXl7DBs4QwCM4DMMw2Y8ry/tqAXMUUIWqomIDZwhwujj+FMMwTLajqRrsdjtCoWCmiyJFCCXpSPSA8zGhLLkIr8ExGYfDAVXhx8owDJML8Ih7/sIjOCbj4tEbhmGYnMHlcqKtLdOlkCNMC9XAU1SMCbCBwzAMkztke59tisS7QOeo2MBJkd7V9goMyCUhDqcVQrIvudogifQkpbJR16CvbUCujtCJr4YooSJRLMQXQpLZOkOl4mARsYYiDnmZDLniJazTF/fH5OX1E7GaujV5upuIM+TUaD9I7pD8fds1+X3bNOJ4VZ5utdDqFZWoCyrxLlTitRpElTIM+pkbhJKDqlNhQmUXIRRtSd83cU6QOMdPlMlPNFVKKQUA/qj8YfmJ2FJ+QpnXLeQxyXpUOtxAAF3S9LAhPycak6uudEOurjIIdRVA90fmxXWi+03yEpIYgnaHNR5DsH8+mY5FZdIanAI1cHixiMk4ndn9NcAwDMMcRNNU2GzsmDUf4REcE7FardA0thkZhmFyCafTgUiE9h+VKYRZwTbZDw4zWJxO+RQKwzAMk704nc6sjCwuRHKHpankU4iwgWMiPD3FMAyTezgc9kwXgYBHcAYDz6eYSPY2EoZhGIaCP07zEx7BMRGHg6eoGIZhcg27PTsXGQukGtGLzqcQYQMnZXpl4jKJot1uSSInTF0KbpY0UVBRDgFQ5dKpYIpEmSjZvKHRMtKYKpe9RtSAND2AYmm6J+qRpjtjSaTaUXnV77IQsm9CBu+yyAdBHSo9OOrQ5Ne2a/Jna1OIdFWebiGOB2jZt5bknFTQkwynU9LyGHFOhHBVECGOD+v0tQllPkJEPQ8QTSYYk99EMEa3b78uzywo5Itae1R5IMyQIl8jEhLd5LWjhlz2HdEJObguLxMlBxfEPfTuM6v/SjW4MEAHKpa3b1UVsFpVRKOH32dfBcmUTNwkJ30F6uiPp6hMQtM0WIgfR4ZhGCa7sdt5iUG+wSM4JsGNg2EYJndxOOzo6ZGPnmUKAQ62ORjYwDEJm03uTZdhGIbJfrLR2Z9ZU1Qci4oZFDyCwzAMk7tk40LjXj84JuQz+CxyEl6DYxLZaP0zDMMwA4P78Pwj5RGc7du3469//St27tyJQCCAESNG4OSTT8aMGTMKQyZ9INjm4QE1rVYVQuigbOVkK/5TVxukKBxMkj+1SyhyFYIBedA93ZBXpVhMrpQCAE2Tz3eHVXmAQL8m91XRo8pVVDbFTV7bZRRJ0x0h+TUcirzzc6jyheXJVFRWIpyHXZPXEQtRd2zEmnZKKQUA1DL4ZOekQrKvTUpnRJ0TIU4ghEwI6/TFo4QsMEQEdg0Z8ouHCNVQSA2S1w6ocpVTRMiVTGFD3i6iulxdGCMCYQKArlNBMuX3QSsuSVkleW2SpOonGfL2kqzfpPrbZPfX24cftv9AsM3MuQI2ydFfmsV/8skn8cgjj6CpqQknnXQSnnjiCZx66qnk8Z2dnfje976H1atXo729HaNHj8by5ctxwQUXpFnywTFgA+eFF17A448/jnfffRdVVVWora2F0+lEe3s7tm3bBofDgauvvhrf/e53MXr06KEsc1bC1j/DMEzuYrVm3zrKXj84mfFkvGrVKjQ0NGDFihWYPn06li9fjvr6emzevBmVlZX9jo9EIvjv//5vVFZW4tVXX8XIkSOxc+dOlJSUDLr86TIgA+fkk0+GzWbDddddh1//+tcYNWpUwv5wOIz169fjpZdewrRp0/CTn/wEl1122ZAUOFuxWnk5E8MwTC5jtVolvnAyR+8iYzMySv2UZcuWYcGCBZg/fz4AYMWKFXj99dexcuVK3HHHHf2OX7lyJdrb2/H3v/89biyOGTNmMKUeNANag/PQQw9hw4YN+PrXv97PuAF6F9iec845WLFiBT755BOMGzfO9IJmOxYLGzgMwzC5TDaO4piFYRjw+XwJWzgsn8KMRCLYuHEj6urq4mmqqqKurg7r16+XnvO73/0OM2bMwDe+8Q1UVVXh+OOPx/e//33oeupObs1iQAZOfX39gDMsLy/H1KlT0y5QLpLPjYJhGKZQyLa+vM8PzmA3AWDz5s3wer0J29KlS6XXbW1tha7rqKqqSkivqqpCU1OT9JxPP/0Ur776KnRdxxtvvIG7774bjz32GB544AGzH8uASXvYYf/+/di/fz+MwxbpnXjiiYMuVK6RbY2CYRiGSZ1sG4k3aw2OAQWTJk3Chg0bEtLNdG9iGAYqKyvxs5/9DJqmYerUqdizZw8eeeQRLFmyxLTrpELKb3Pjxo2YN28ePv744/gqdkVRIISAoigZHY4aDsSByUxFHDTsLJp6cKKUqItmxWVJCnENkXQCVq6OECk7XyDUVZArRQAgRsQOihBaH0WVN0aVUDhZLLSqz6rK1VIW1SVNpxRZNsjzsenyfADARsTIsgl5upVophZFPgCrJekQNeIcStiiEHlRdSpZNdeFvK7pRF4x4vgoEfcsotBxkah9EUWuTIooclUUpXyKxeT5AEDUkOdFKQxJhROhlhJJ4twNdb+TPB4UUdeoIqWsrqJJ+b4PHG+1aAeVUzhYz5P3obmBqqooLpbH8zuciooKaJqG5ubmhPTm5mZUV1dLz6mpqYHVaoWmHey/jzvuODQ1NSESiWREiJOyH5yvfvWrOOaYY/D3v/8dn376KbZv357w30Ik26x+hmEYJnWyLp6gOLjQeDBbqvaZzWbD1KlT0djYGE8zDAONjY2YMWOG9JzTTz8dW7duTZjV+c9//oOampqMqYxT/mX+9NNP8etf/xoTJkwYivLkJGzgMAzD5D7Z1pebF4sq9TwaGhowb948TJs2DaeeeiqWL18Ov98fV1XNnTsXI0eOjK/jufnmm/HjH/8Yt9xyC775zW9iy5Yt+P73v4//+Z//GXT50yXlt3neeefhn//8Jxs4h3DokBzDMAyTm2RbX947+JKZYJtz5sxBS0sLFi9ejKamJkyZMgVr166NLzzetWsX1EMcmo4aNQp/+MMfcNttt+HEE0/EyJEjccstt+C73/3uoMufLikbOD//+c8xb948fPjhhzj++OP7LbC98MILTStcrqARnmkZhmGY3CHbDJxMs3DhQixcuFC6b926df3SZsyYgbfffnuISzVwUjZw1q9fj7/97W/4/e9/329fISwylsGNgmEYJvfJtr7ctGCbub9GOi1SNnC++c1v4pprrsHdd9/dTyNfqCQ2CipOVCZHeejYVcOi7krx2ofH+Yqjy9MppVaUCjsDgIqQpahyyb9Kqq7kSi0LETer9xy5GsyiyNM1QiWmKURZkzRrVZHvU8koValhEDHMAMAg3iuVrkPuUVYnVEbJYjJR+2I6oXAyCIUToYgSBu39Np9/W5LHg6L6nUwaEcnj+GWdgZPBNTj5QMoGTltbG2677TY2bg5BTRJYkWEYhskNsq0vF1AytgYnH0j5bV566aX405/+NBRlyVmyrVEwDMMwqcN9eX6R8gjOMcccg0WLFuGtt97CCSec0G+RcSYlYZmCGwXDMEzuk219Oa/BGRxpqag8Hg/+/Oc/489//nPCPkVR2MBhGIZhGJMwZ/1Mdq/BGTt27BG8Ysu59dZbk9ocKRs427dvT7kQqfLkk0/ikUceQVNTE0466SQ88cQTOPXUU8njX3nlFdx9993YsWMHJk6ciB/84Ae44IIL4vuFEFiyZAmeeuopdHZ24vTTT8dPf/pTTJw40ZTypvNiGIZhmOyjL/RQNiBgzgiOGXkMJc8++2xa540ZMybpftPcNu7btw/PP/88br/99kHls2rVKjQ0NGDFihWYPn06li9fjvr6emzevBmVlZX9jv/73/+OK6+8EkuXLsWXvvQlvPjii7j44ovx3nvv4fjjjwcAPPzww/jRj36E5557DmPHjsXdd9+N+vp6fPTRR3A46HhFAyVbDBwyXkqWNNZsgnoilBrGoNKJWEYxg65XKqWW0oiYWtTxqvx4RaGVIBrV5IkYVSlDxI8CAJ1QSwkhV17pBqGWEkTcM52ORWUQ5xg6FQ8qiQSPGRiUSpIIRkXFPRtussnAKRTOPvvsIclXESm+ya9+9avS9J07d+Kdd95Bd3f3oAo0ffp0nHLKKfjxj38MoDf+xahRo/DNb34Td9xxR7/j58yZA7/fj9deey2e9vnPfx5TpkzBihUrIIRAbW0tvvWtb+Hb3/42AKCrqwtVVVV49tlnccUVVwyoXD6fD16vF4AdiqIkNMbjj/+vg7E2SGPHzGksIkAm9eNC/IAAhbu63ixI2bXGBs7hsIFTWJDmClE/FbIODn3feagx9s9/fhD35xYPtikEgDC6uroGHLBysFx11VVwvdWEy2o/P+i81jT9A82fK8Lq1atNKFnukPIITkdHR8Lfuq7j008/xccff4yf/OQngypMJBLBxo0bsWjRoniaqqqoq6vD+vXrpeesX78eDQ0NCWn19fVYs2YNgN4ptaamJtTV1cX3e71eTJ8+HevXrx+wgZOMbBnBYRiGYQZHNvXnacTJzFkuvfTSAR2XipGWsoHzm9/8Rpr+4IMPYs2aNbjxxhtTzTJOa2srdF3v52OnqqoKn3zyifScpqYm6fFNTU3x/X1p1DEywuEwwuGDX30+n2/gN8IwDMMwzIDpnSE5yIsvvojZs2ejqKgo7TxNW4Nz5ZVX4oEHHjAru4yzdOlS3HvvvZkuBsMwDFOg9C4yNsHRnwl5DDXPPPNMwt+vvvoqHn74YYwbNy7tPE2b3PznP/+Jk08+eVB5VFRUQNM0NDc3J6Q3Nzejurpaek51dXXS4/v+m0qeALBo0SJ0dXXFt88++4w8lhekMQzD5AfZ1J8L9K4aMmMrRFIewTl8vQvQayz89re/xaxZsxL2L1u2LKW8bTYbpk6disbGRlx88cUAehcZNzY2khFNZ8yYgcbGRtx6663xtDfffBMzZswA0Kuvr66uRmNjI6ZMmQKgd7ppw4YNuPnmm8my2O122O3yxZ0MwzAMM9QIoZg0+pL9IzhDQcoGzvvvvy9NP+WUU7B//37s378fQPoLtRoaGjBv3jxMmzYNp556KpYvXw6/34/58+cDAObOnYuRI0di6dKlAIBbbrkFZ599Nh577DHMmjULL730Et5991387Gc/i5fj1ltvxQMPPICJEyfGZeK1tbVxI2qwZIvFT8ksRbJ3kSVlH26oJ8LBNgeHgSTBNgllEhlsU00x2KaWRrBNlYNtDhlEv5MtcnAKwyjU8Y78I2UDZ6jjUM2ZMwctLS1YvHgxmpqaMGXKFKxduza+SHjXrl0JnoNPO+00vPjii7jrrrtw5513YuLEiVizZk3cBw4A3H777fD7/bjhhhvQ2dmJM844A2vXrjXFBw6QPQYOwzAMMziyqT/vm6IyI59s53e/+13C332zNx9++GFC+oUXXjjgPFP2g1OoJPODc+yxk+ByuXr/yKAfHNKxVpKv6kJ9/TyCc/g5WTiCI1IcwSF83QBJRnB0HsEZKqhRfIWqa1nSd7733sFZikz7wbH8pRmX1MwYdF6vN/8D7dPcWe0HZyAhjxRFifsoGlCeAzlo5syZePvtt494XHd3N37wgx/gySefHHAB8gEe0mQYhsl9srEvF1BM2LIfwzCOuKVi3AADnKK67LLL8OUvfxlerxezZ8/GtGnTUFtbC4fDgY6ODnz00Ud466238MYbb2DWrFl45JFH0rrBXCUbGwXDMAyTGtyX5xcDMnCuv/56XHPNNXjllVewatUq/OxnP0NXVxeA3iGjyZMno76+Hv/4xz9w3HHHDWmBsxFuFAzDMLlPtvXlZgXbzPYRnIxHE7fb7bjmmmtwzTXXAOiN5xQMBlFeXg6rVb4WoFBIHDYzc77YLOgyKQoV12rom0Sqc/QKsXZFJdaoWCz0InIruabGJU23qW55uiLPxybk+fTuk5eXSrcKeTO1EO9VS6JS0YhzqL6FVOYRXWayaqMTcap0Iq8YcXyUWLMTUelYVBFNvi9ikwdLjQj5WpuI4Zemxwx5PgAQJdbtxGJUHCx5WQWxjiiTa+yS/yjlUl/Y+wxTnQIZavqmmMzIJ5vJumjiXq+3n2vlQiXbrH6GYRgmdbLOwBEmjeBk+RDOUEUTz0YTO+eIxbKrUTAMwzCpk20GDjM4TItFVchwo2AYhsl9sq0vN20NTpaP4AwVbOCYQCwmXxPAMAzD5A7Z1pcLmLN+pkDtG56iMoNsaxQMwzBM6vByg/wi5RGcefPm4frrr8dZZ501FOXJepQD/4Ny0DaM6UYSL5wHzkuy3zSlA6VKSpo9pVhKzauoQnnIJbztAoCmEV6ACbWUVZMrk+yqR5puU+TKJwBwiSJpusOQq6IchMLJocifn0Ojvx2sxD67Jn/mFuJV2AiHsOSrA/W2k5+TCsmG06mfDuqcCHFCjDg+rNMXj+pyIUCIEAiEhPziIcgVTiGLXCkFAAGlW5oesckVWWGjR5oe1eVKLcpLMwDoOqXUknteFoS36VS9pyclzTiFqV2CUP9R5T1wfG9ffrB9Koco+TIxCmLaIuPBZ5GTpDyC09XVhbq6OkycOBHf//73sWfPnqEoV04RjdJu2hmGYZjcINv68r5YVIPd2MAZIGvWrMGePXtw8803Y9WqVRgzZgzOP/98vPrqq1lXOYYLnqJiGIbJfbLvN0yBEIPfINIbNXvyyScxZswYOBwOTJ8+He+88w557LPPPtsbp/GQzayA1umS1hqcESNGoKGhAf/85z+xYcMGTJgwAddeey1qa2tx2223YcuWLWaXM6sRQmTd6nuGYRgmNbLPwMkcq1atQkNDA5YsWYL33nsPJ510Eurr67F//37ynOLiYuzbty++7dy5cxhL3J9BLTLet28f3nzzTbz55pvQNA0XXHAB/vWvf2Hy5Mn44Q9/aFYZc4JIhBsGwzBMLpNtBo5ZU1TpuKJdtmwZFixYgPnz52Py5MlYsWIFXC4XVq5cSZ6jKAqqq6vjW1VVVRpXNo+UDZxoNIpf//rX+NKXvoTRo0fjlVdewa233oq9e/fiueeewx//+Ee8/PLLuO+++4aivFlLtjUMhmEYZuBkYx9uCHM2gV6P+z6fL2ELh+UL1CORCDZu3Ii6urp4mqqqqKurw/r168ny9vT0YPTo0Rg1ahQuuugi/Pvf/zb7kaREyiqqmpoaGIaBK6+8Eu+88w6mTJnS75hzzz0XJSUlJhQvC1FUQFH6qYaiUQOKooHWqdBTWCmv+E/VLk0y/UrGgyLiO1GqKE0j4itptJLJSsR3ciiEwgnF0nSPIVdROYl7AAC3Jq/6TkKa5CSkTC6iBTmSvCIHUUXsmvx92wgZnE2Vp1uSyOYotZSWXGo3YPQkc/2UGiRGnBMxiHTi+LBOXzukyx96yJCnB2Lyeh6MyRV+wRgde8yvy0PaBImYUz2qXEUVUn3ydCFXaQFAlIidFdHl6bouLxOtuqLjf2U2DhZ1DvWTpyMaDffbL+Jxz0TGVuqaddnNmzf3C6+0ZMkS3HPPPf2ObW1tha7r/UZgqqqq8Mknn0jznzRpElauXIkTTzwRXV1dePTRR3Haaafh3//+N4466iiT7iI1UjZwfvjDH+Kyyy5LuniopKQE27dvH1TBcg3KEmYYhmGyn0iENtbygUmTJmHDhg0JaXa73GBPhxkzZmDGjBnxv0877TQcd9xx+N///V/cf//9pl0nFVI2cK699tqhKEfOk++Ng2EYJp/JxnWUvaEaTPBkLHqnmIqL5aPgh1NRUQFN09Dc3JyQ3tzcjOrq6gHlYbVacfLJJ2Pr1q0pl9cs2JOxSYTDbOAwDMPkKtk4Ci9M3FLBZrNh6tSpaGxsjKcZhoHGxsaEUZpk6LqOf/3rX6ipqUnx6ubBsahMIhsbB8MwDDMwsvEjNZOejBsaGjBv3jxMmzYNp556KpYvXw6/34/58+cDAObOnYuRI0di6dKlAID77rsPn//85zFhwgR0dnbikUcewc6dO/G1r31t8DeQJmzgmISu69B1A5pGLTJmGIZhspVQSB7WolCZM2cOWlpasHjxYjQ1NWHKlClYu3ZtfOHxrl27oKoHJ4E6OjqwYMECNDU1obS0FFOnTsXf//53TJ48OVO3AEUM9VL3PMHn88Hr9UJRPAe8NPZXWUyaNBEej1wZlPwxp+YkMNVXRisHcED51R9NlSuQLKo8VpPNIlcy2VV6ztcFubrEY8jPKVLkC9vdFrnixW2hZ2DdVvm8dhEROstN2K1ui9zDhFOjPU+4Nfn7thPn2KjjVXm6lSgTAKjEt5yqys+hVFfUV6Vh0M/cIOR80Zj8nDChcIoQiqiwTl/bT5wTJM7xE2XyE021O8nyDX9U/rD8Mfkz98fkmXUL+Q9wD6GuAoAAuqTpYUN+TiQmV3DFDHmsLd1IpqKSPyw63pWcdNRSZIw9Ii9d17Fp07/6pYsD6jEhBIToQVdX14DXsQyWq666CoH/a8F/jzhj0Hmta3sb+uedWL16tQklyx14DY6J8BcAwzBM7pGN01N9CGHOVoiwgWMioRCvw2EYhsk1+OM0P+E1OCbCjYRhGCb3CAazs+8WUMip3VTzKUTYwDGRbG0kDMMwDE0wKF9jlGnMml4q1CkqNnBMJBKJwDAEVGplJsMwDJN1ZOvHaV+wTTPyKcRfJV6DYzKBQCDTRWAYhmEGiGEI9kSfp/AIToooigpFUaAS0utwKIYij2SfQtvh9PBhavJxOnAmbbuTwTNVeYwSq0Uug6fk4B6Uktem5OBeQoruscqln0VWuZ3upWNtknLwIov8mRcT0usiq1zS67bQumGnTb7PYZPLZ612eZksNnmZNBs9Hk15DCC8BdCfQER1JpTBvfsIdbAekdfPWER+8WiYCJwZobuzYET+wv1EUM3uqDzdR8jH3Rba/1U3Eai1KyI/x0qMAFui8uPVJNL8Q/2UJO6QJwuLvO4IQtKezGWFQY49pBpcmIJ+5pT7C0Vy4+FQgOzPjXgfLDIyzdMbqsGcfApxBIcNHJMJBLJzLpdhGIbpTzb32emEWWAOwgaOyWRzY2EYhmES8fuzd1mBaaEaCtRK4jU4JhMMBmEIM5aFMQzDMEMNf5TmL2zgDAFBbjAMwzBZjyGM7PZfZpIX4wIdwGEDZyjI5iFPhmEYppdAlvfVfTLxwW6FauDwGpyU0QAoUBS5bagoFgQCkf6r+EWSNezELmreVFFSU1cls2MphZVGqKssilxdZVPkyienIVddAYCHyItSS3ltqamlSq10s/Za5c/Qa5VLfbw2eRgOj0OuiHK66LAddjehipLHK4Xilt+36pC/I8WeJKI98WyVJIFJU4FS2wAAokTwxbA83QjJ34XwyyW97h5a6hv2y+/bE5DXQXdI/mydEfnxDpWuazZC0aMSbU8l+hYKnSgTAOhC3v4iqnzUIqbI03UyPUl4GpHafdBqTypwZhIVFXlOYpl6+2oLAELiJ/ryyYyJYJqKqkAtHB7BGQJ6evyZLgLDMAxzBLivzm/YwBkCYrFYVkenZRiGYbLfwBEmboUIT1ENEX6/H3Z7Ek9zDMMwTMYIBkMwjOxWvJomEx98FjkJj+AMEd3dPZkuAsMwDEPQ08N9dL7DIzhDRE9Pdq/OZxiGKWS6u7N7egrom14afJAFM/LIRdjASRFF6VNQEcqWAzFNYlEDsagBm+2AGiPJin+dDNlCxX8ZaGmPjCw2CwCoqrxqWBT5tJtNuKTpdkFP0zmJ+D0uQtFDxY8qJtRSlFIKAEqJeFClDrkqpMgpV5G4vPJ8rCX0S1KL5M9WLZY/K8XjkGfkJJ5tsqlRG9HkNaJ+UsoWohIqehKFX0SuVFGI9WpqUJ4ueuTvQvXR694s3fJr2zrlPqusXfLjLUH5fauEIjAZOqH0MQjFZZSIORXR6b4lFEutvUYU+Y9+lOgPFH3oJwHIGHtJYlFpKtFZHELAH4731zrhnPWg6iozU1msohocWTVFJYTA4sWLUVNTA6fTibq6OmzZsuWI5z355JMYM2YMHA4Hpk+fjnfeeSdh/znnnHPAMDm43XTTTUN1G3F8PE3FMAyTdQQCQejJjPEsoW8NzmA3NnCygIcffhg/+tGPsGLFCmzYsAFutxv19fVJPU2uWrUKDQ0NWLJkCd577z2cdNJJqK+vx/79+xOOW7BgAfbt2xffHn744aG+HXT7uof8GgzDMExq8BrJwiBrDBwhBJYvX4677roLF110EU488UT84he/wN69e7FmzRryvGXLlmHBggWYP38+Jk+ejBUrVsDlcmHlypUJx7lcLlRXV8e34uLiIb6j3JjjZRiGKTR8vtwxcFginj5ZY+Bs374dTU1NqKuri6d5vV5Mnz4d69evl54TiUSwcePGhHNUVUVdXV2/c1544QVUVFTg+OOPx6JFixAIJF8EHA6H4fP5ErZU0XWdwzYwDMNkEYZhZL3/mz761uAMesv0jWSIrFlk3NTUBACoqqpKSK+qqorvO5zW1lboui4955NPPon/fdVVV2H06NGora3FBx98gO9+97vYvHkzVq9eTZZn6dKluPfee9O9nTg+XzfcbvmCPoZhGGZ4yaW1kb0jMGaMwRTmOE7GDJwXXngBN954Y/zv119/fciudcMNN8T/fcIJJ6CmpgbnnXcetm3bhvHjx0vPWbRoERoaGuJ/+3w+jBo1CgpUKFBJlZGmJqoWerrDUEcmX9EvCPvaIJbPm1Ph00U+6GchFA3WJOoxmybPiwql5CTSXZr8+XksRHwZAEVWueLGQ6mlSuTH20qJeELltKpG9RKqKC8Rt8stj/MFwnAWSVVUxD7LkVUnAyImV5UBACLyZ0ipqECMfioeufJJc9Jf5YpD/l4Vlbi2Qii4iPxjOi3DjRryfSEiPUwo2qh2QbUjALASCiuqvQ7PoH6SWGkSFOJnSlXpfNQkKip/d7hfP031wUKn+xAm+8mYgXPhhRdi+vTp8b/D4V55bnNzM2pqauLpzc3NmDJlijSPiooKaJqG5ubmhPTm5mZUV1eT1+677tatW0kDx263w25PXfp5OMFgCNFoDFZr1gyWMQzDFCxdXbkj/jDNk3FhDuBkbg1OUVERJkyYEN8mT56M6upqNDY2xo/x+XzYsGEDZsyYIc3DZrNh6tSpCecYhoHGxkbyHADYtGkTACQYUkNJV2fq63cYhmEYcwn4g4jFcmdUhmNRDY6sGVZQFAW33norHnjgAUycOBFjx47F3XffjdraWlx88cXx48477zxccsklWLhwIQCgoaEB8+bNw7Rp03Dqqadi+fLl8Pv9mD9/PgBg27ZtePHFF3HBBRegvLwcH3zwAW677TacddZZOPHEE4fl3rq6fKgYUTYs12IYhmHkdHbl1semaY7+Bp9FTpI1KioAuP322/HNb34TN9xwA0455RT09PRg7dq1cDgOrlnYtm0bWltb43/PmTMHjz76KBYvXowpU6Zg06ZNWLt2bXzhsc1mwx//+Ed88YtfxLHHHotvfetb+PKXv4z/9//+37DdV3e3PyecSjEMw+QzPJqeGkdyokvx0ksvQVGUhMGJTJA1IzhA7yjOfffdh/vuu488ZseOHf3SFi5cGB/ROZxRo0bhz3/+s1lFTJuuzm6UlZdkuhgMwzAFSTAYQpha0J6tmOSFOJ08+pzorlixAtOnT8fy5ctRX1+PzZs3o7Kykjxvx44d+Pa3v40zzzxzECU2h6wycHIBRVEPiU8i2y9f2d/jC2FEBbGyn8hOUPF+qBX/gholor0gkOoBIjYLhSKImFZJzlGJGDNEKCoy3aHKn5ODUFcBgJOIRWV3yOfnrUXyfFSv/J2SSikAKCUyK5GnC49HfjyhrhLOJG4JrISKSjUpGF+y8fQo8eMSJHxFOeTPULETMl+Vrm3knpi8vNaYvKz2kLx+OCO0eswRk9cRqt6mWv+pdgTQ9021VyomI9UfUP1HLyn2IUTfSaVrKi0C0ZT+z7ynq5PMiy5TZic5BMzxYZOOjXSoE10AWLFiBV5//XWsXLkSd9xxh/QcXddx9dVX495778Vf//pXdHZ2pl9oE8iqKap8xufrgU5G1WQYhmGGks4Cn54yDKOf89o+9fLhpOJE91Duu+8+VFZW4vrrrze9/OnABs4wkkvyRIZhmHwhFArn3vQUzPNkLABs3rwZXq83YVu6dKn0usmc6FKOd9966y08/fTTeOqpp0x+CunDU1TDSGdHF8rKvJkuBsMwTEHR0d6V6SKkhTArErgAJk2ahA0bNiQkm+HrDQC6u7tx7bXX4qmnnkJFRYUpeZoBGzjDSHe3H7FoDBZ2+scwDDNsdHTkqIEDc9bgGOidYhpokOlUnehu27YNO3bswOzZsw9e0+gtucViwebNm0mnukMJT1ENMx0dhT0PzDAMM5z09AQQjeaOc79sIFUnusceeyz+9a9/YdOmTfHtwgsvxLnnnotNmzZh1KhRw1n8ODyUMMy0t3diRCU7/WMYhhkO2ts7M12EQWHWFFWqHMmJ7ty5czFy5EgsXboUDocDxx9/fML5JSUlANAvfThhAydNFGLwi5IV9kkaoxEgHDLgch2UwFKSTVLCHSMGLZXUBzMpKTopCxXyLyFdISTqaTQsSvSqEplpCpGu0s/DapHvs9gJab6deN8uogm5ksxtU/JuKuq8Rx6Ek5SPE8Eah4VkcnNivl9Y5M+QaktU3VSSfaUTC0yVoFzerfTI86LqB1WfALoeUvWWqucK2TJSh2qvVPum+4PUG7hCyNoVIginpsldG1iSyMRVpbdOGYaB7q5wvP/tmzbpf+3snMwwkDmZ+Jw5c9DS0oLFixejqakJU6ZMSXCiu2vXLqhJXDNkA2zgZICO9q4EA4dhGIYxn87O7rSMsKxBCFPKL9IM1pDMie66deuSnvvss8+mdU0zyW7zK09pb+8ivyQYhmEYc2hv68x0EZgMwgZOBjAMA11dhDdWhmEYZtAEg2H4/cFMF2NQCBN84BhmSc1zEDZwMkRba0emi8AwDJO35MPojTBxK0TYwMkQfn8QwaDcTTbDMAyTPoZhoD1Hnfsx5sGLjFNGBZA84Kb0LKX/o+5o64ZrlAsWRa4SIJULmtwe13VCEQVaXUIF6NSFXF2iC7kaJaYQ6UnGRg1inyHkKguDUJEIE9UlIJQtsBDKDwuhWErmzNFKBF21EfWASM+oWspMiPug7luhngf1XAHyfZDvj3p9VP1IA6reUvWcimNKtSOAbn9Ue6XaN9Uf0AF+aRTi4WqaXBVlUYh0lQ4q29bWDggV6gD7abo/z4Jgm2ZEEx98FjkJj+BkkPb2LhgcgJNhGMZUWlvyYwkAr8EZHGzgZBAhBA+jMgzDmEhPTwChUP5M/wsT/leosIGTYfLlS4NhGCYbaGUBB3MAXoOTYSKRKLq6uuH1FmW6KAzDMDlNJBKBL49ccJi1BqdQF0LwCE4W0LK/PdNFYBiGyXla9ufX6I0Q5myFOkvFIzhZQCQoEA7E4Do8RpEmjzVEzamScaKSeE2m4l0Zhlw1ERPyue2oIk8PQ54PAER0ueolasjtbioEF5WuE/kAgEHsEzqhyCKEaIKKbZP0syvF3ibL470MGSnfd5LnSrwP6v2R75uoH1R9Auh6mGp9jhLpkSRCBar9Ue2Vat9Uf0D1H73I71tV5T87VGwpK9EPakpi/6HrOnwdIVgUO6IikKRcuYOAgGGCdVKo63AKtOfMPvbn2ZcHwzDMcNLa0pnbcacY0+ERnCyh2+dHKBSBw0H4+GAYhmGkGIZAW2tnpothOgLmSLwL1e7jEZwsoqWZ1+IwDMOkSntbJ/Q89Ckm0LtAeLBbgdo3bOBkE52d3QiH5Z5EGYZhmP4Yhsi7xcVxRK+/tEFvmb6PDMEGTpbR0pynDZVhGGYI6GjvQiyWesgIJv/hNThZwKFqAF9nELEaAbvNCo2KUQV5YyZjVyWNB0XEnCJUE1E9KE2PqHLfEyG1mLx2UJerJoIxeXygEKFgCREqlZBO2+9h4hqxiPwcW5h45iG53EaJ0PG/QIzSKYT3VeEgRvWsxHot1cTYXMMBpThL8TlRxwMAiPdBvT8RlrelWEReb6j6BND1kK638nyCMflzCur0j3tIJdqrIW+vVPum+oNkqIpcJWlRndJ0qyaPLWVT3cQVjN61N/u7+ymqUi9tdmJaLKoCHcLhEZwspHlfW6aLwDAMk/W0teX36E2fTHywW6FOUrGBk4V0dfYgFOK1OAzDMBSGIViYwSSFDZwshUdxGIZhaFpbOvJSOXUopnkyLlB4DU6W4vP5EQyE4HQ5Ml0UhmGYrEKP6WjZ35npYgw5vTLxwVso+W0G0vAIThazb29rpovAMAyTdexvboeRJARNvtDn6I9jUaUHj+CkCaVYIo9Pw4aOBVSEfDqKvIkqApVSyRBvM9m1o1TgG6JF6AYRc8rolqb7LT7y2k5DrppwxeQ30kMoVVwxuZ3u0ujq7Y7IFR7OoDzd2i1fyKi45XoNxSFXowCAYiPKZZWnK5r8vsk+yy1/rgAAIq9hgVL7+OVxg5RueZ2C3y9P76bjDwmf/H0YPYRakLh0iKgfPUR9AoAeoj53E/W2hxDg+Ym26hf0ej2/Km9/YV1+g1T7pmqbAro+WTT56LPNIo8t5VS90nRNSVRbRsJRdLcl10ml2t+m2p8zuQEbOFlO0752eIpdUJQck/4yDMMMAc37CmdhMQfbHBxs4GQ5kXAU7a0+lI+Qf90wDMMUCgF/CL4uYgQvDxECMExYJVyoBg6vwckBWpo7oOexrweGYZiBsG9Poa1LFCb9rzBhAycH0HUD+5s4hAPDMIVLR5sPoSD7B2MGDhs4OUJ7mw9hdv7HMEwBYuhGQa296YOjiQ8ONnByiL27C214lmEYpndhcb479ZPR5wcnU6EannzySYwZMwYOhwPTp0/HO++8Qx67evVqTJs2DSUlJXC73ZgyZQqef/75NO/cHHiRccokb2R0wEv6PCEICbKSaH8G/RF0tftRXloiz4gwV4WFrtyUnDIWC0nTqaB7EV2+8C+o0F9d3UTQPUeMkHATgTAdhGzeodISVocmD1RpD8jfhcUif06qjQgIqdEycS3FYJgK0bErMSpQpPzdAYCwE44jCYk6VKJSUT5IonSQUYUolxIgnlW3PCAkuoj0diIdgNEhv0asXX4fQZ+8fvgC8gCxXREi8CmArqi8HvqIAdnuiLy9dsfkba9b7SKvHdTl7Y9qr3RQTXmdtVhoR6Q2S5E0nZKD2yGXj/uDAXS09UA5rIOj+s3efan3w3KMw/47vAiIpMGSB5xPGlmsWrUKDQ0NWLFiBaZPn47ly5ejvr4emzdvRmVlZb/jy8rK8L3vfQ/HHnssbDYbXnvtNcyfPx+VlZWor68f9D2kA4/g5BhNe9ugk75rGIZh8gseuc4My5Ytw4IFCzB//nxMnjwZK1asgMvlwsqVK6XHn3POObjkkktw3HHHYfz48bjllltw4okn4q233hrmkh8kqwwcIQQWL16MmpoaOJ1O1NXVYcuWLUnP+ctf/oLZs2ejtrYWiqJgzZo1puSbrei6gaa9hTcXzTBM4dHe6kMwQDkfzH/MjCZuGAZ8Pl/CFg7Ln20kEsHGjRtRV1cXT1NVFXV1dVi/fv2Ryy0EGhsbsXnzZpx11lmmPY9UySoD5+GHH8aPfvQjrFixAhs2bIDb7UZ9fT1CIXrI3e/346STTsKTTz5par7ZTFdHD/w9uVl2hmGYgRCN6Ni/r7DVo+atwQE2b94Mr9ebsC1dulR63dbWVui6jqqqqoT0qqoqNDU1keXt6uqCx+OBzWbDrFmz8MQTT+C///u/TXwiqZE1a3CEEFi+fDnuuusuXHTRRQCAX/ziF6iqqsKaNWtwxRVXSM87//zzcf7555ueb7az97NWTJg0EkqK6zkYhmFygX27W2EYglr+w6TIpEmTsGHDhoQ0u12+pixdioqKsGnTJvT09KCxsRENDQ0YN24czjnnHFOvM1CyZgRn+/btaGpqShgS83q9mD59+oCGxIY730wTjcTYNw7DMHlJV4cfPd30Qv1CQZg2SWVAVVUUFxcnbJSBU1FRAU3T0NzcnJDe3NyM6upqsryqqmLChAmYMmUKvvWtb+ErX/kKOUo0HGTNCE7fsFeqQ2JDlW84HE6Yn/T5EoPWUeojamW/AL3iX4dcuaALebrjgNqgqyWM0hIBp6u3krpQIr9AMjM2xRpAqatiujw9pNDBNrsVufLECnmjsxAqKosqV11ZkoxuaYr8xjXIVSFUKDBFkcsTXKQaBRCEgkUjvFUrlP8jv/wHQHHL1WkAoNgJtQ+lolIoaV7qKiqEU7sP9MjTqcCZejs9bRtrlZc30CGvOx3d8mfYFpLXj9Yw3ZDaI/LK00Gopdoj8rrTCXmAzG7RQl47pMvbH9VeKSi1lN1Ch5BxqSXydKqfOtBR6TEDrXt6YDnQD4SFPIgq1W8CdH9L98/ZKdwwLxZVathsNkydOhWNjY24+OKLAQCGYaCxsRELFy4ccD6GYZDrfIaDjI3gvPDCC/B4PPEtGk0eHXa4Wbp0acJc5ahRozJdJCl7d7WmpwFkGIbJQvZ91lqQPm9kZNIPTkNDA5566ik899xz+Pjjj3HzzTfD7/dj/vz5AIC5c+di0aJF8eOXLl2KN998E59++ik+/vhjPPbYY3j++edxzTXXmPU4UiZjIzgXXnghpk+fHv+7z8prbm5GTU1NPL25uRlTpkxJ+zp9w2mp5rto0SI0NDTE//b5fFlp5ETCMTTv60RVbWmmi8IwDDMofB1+dBMjdMzwMmfOHLS0tGDx4sVoamrClClTsHbt2vhsyK5du6Ae4ifL7/fj61//Onbv3g2n04ljjz0Wv/zlLzFnzpxM3ULmDJyioiIUFR10BCWEQHV1NRobG+OGh8/nw4YNG3DzzTenfZ2xY8emla/dbjd9AdZQ0d7iQ1GxEy4P7WyMYRgmm4lFYti3uy3TxcgqeqeoBj+ala4n44ULF5JTUuvWrUv4+4EHHsADDzyQ1nWGiqxZZKwoCm699VY88MAD+N3vfod//etfmDt3Lmpra+NzgABw3nnn4cc//nH8756eHmzatAmbNm0C0LuoeNOmTdi1a1dK+eY6e3a1Qtd5qophmNxkz2dtvaop5hAEhGKYsBXmc82aRcYAcPvtt8Pv9+OGG25AZ2cnzjjjDKxduxYOx8EFbtu2bUNr60HPlu+++y7OPffc+N9900rz5s3Ds88+O+B8c51YVMe+3W04anRFpovCMAyTEm0tPgTYt1c/zFtkXJgGjiLMCHRRAPh8Pni9Xli0CiiKClWVTwdZNLnRZNPcZN52rVia7lDl6R7IjRiHcKFmVBm8ZYnXiim0sqUHcjfoAaNTmh6OyePe6DoRk4mSHwGwW+T357KMkKaXiRppeiUR26bCIVfIAMAIwratsMmHgyvs8mdY7pArBEpd9DoCd7H8WdlLiXhXXvl3iEpMSSpu+r5JtZSViNuVciwqWi1IKayEXy4wMHrkz8nokucT7qAHpP1EbKmOAKWWkk9PU2qpVkLhBwAtxO92a0h+3/sNeRtrV/ZJ0wMxWkUVjslVVFS3r1Ex2gi1FKWUAuh+yiISn2EoGMWO/zQhpMjVUlQfFTJohWaYUI9RMbgoVZlh9NZBIQzE9FZ0dXWhuFjeb5nNVVddhbd+swM1js8NOq+m8D8xdeYIrF692oSS5Q5ZNYLDDJ7mPR1weeyw2vjVMgyT3RiGwJ4dHGuKwsjwGpxcJ2vW4DDmYBgCu3e0snKcYZisp+mzdkQjSXwnFTzmOPpL3RNOfsCf+XlIOBhF854OVB/F0nGGYbKTznY/fJ3yaSmmF6EIGMrgR3DMWMeTi/AITp7S2daDbu48GIbJQsKhKJp3c6gZZmjhEZw8Zt9n7bA7bVDzRyzGMEyOY+gCu7e3kgudmYNk2g9OrsMGTor0xSwRRCweKl0X9DwzFXNKh/ycqCJX7jiEK+FvwxDYvb0FxxxzNFRCzaSqcsWSospVNYpFnk8Yqce8icR6yH1SqNpKtH8RpOPk6IY8s6guH9SMCrkyKWLIjw/FCFUSgJKI/P15eoj32iGvB1a3fIROcyYJv+yQ71Ms8vtINRSViCXpjEPyTlYPytOjfuLZ9siVPj2E8gkAOol9HRF5Xm0R+ftrDcmfX1uY/gFpD8vfX4swRy2VrB1RRgSl9iSVjWqZNL0I8nQAcBhyhdqnu/ZK191Q/RrVd1L9ZrJzUu234/19hmJVsYEzOHiKKs+JhGPYt5O9gzIMk3lam33o4VAMzDDBBk4B0NMVQmsz7TOCYRhmqOnxhdDWxP1QKghWUQ0KnqIqENqafLA7rCjyyoeNGYZhhgoeSU6P3gmqJM4zB4gZ01y5CI/gFBBNu9oRJrynMgzDDAW6bmD3py0cZyoteARnMLCBU0AYhsDuT1uh64VpzTMMM7wIAHt2tCEaGfwoBMOkCk9RpUivKsEACOdLqa7SBwCDWPEfE3IFUhREuiKP3eMUBxUTRtTA3k/bMGrCCCiKAo/hkZ5jUeSqIVWTVxkFctVJGHKlCADEDPliQ0oVQj5bC/H8CBUaAERCJfJ0Q66qCRFqqUBM/jx8UfrboSsqf7ZeQunj7pGPujlt8nS7jb5vq5WId6WZY/QahAoNAKLEMwlH5M8wGJE/J39Mnk49VwDoJFRRXUSZ2uVNCR1h+XNqjxAnAGhTOuXXBqGWisqncqK6XDWXTOFj1VzSdCq2lFsrl6Z7DXm6Q9DKNR0Gmne1I9wTgYqD6jOqn4oS/R3VD1L9JpBOP5z8+ExJ2g2Y4+iPo4kzBUMoEMG+ne2oHSPvtBiGYQZLW7MPvg52NjoYzFqDkymZe6bhKaoCpacriJa9nZkuBsMweUhXR4AVU6Zgzhoc9oPDFBwdLT3oaE3R2R7DMEwS/N1hNO/iMAxM5uEpqgKnZU8XNIuK4hL5XD3DMMxACQWj2LeD5eBmISBgCBOmqJKsAc1n2MBh0LSzA5qmwV1ELxhkGIZJRiQSw+5trSwHN5GDMu/B5lOY74SnqBgAwN7tbQgGaCUIwzAMRTSqY/fWVhjsgsJkBAT0QW9kwL48h0dwUkYHoJBDfpR00UgSGI7aRwWMixFB6SKQy67DCh1OvFi4e/8hgOZt7Thq4ohej8eQT1lZdHmVsahyiW6PlZbuhnW5hDxCyGEpmWwPMYSrW2iDLaLJn1UwLFeWBWLy59ETlcuPvTb626GTkCYXWeTP1m2RS9ddmvyrzKHRQ9o2VV5vNUqKSsXtJD4IdUHfNxmYVJc/w4Auv7ifCGTaHaODjPqI5tcVkd9IF+G3pYOog50aPS3To++XpgdjndJ03ZC3byryqV2Tu3ro3SeXg3sUQg6uy4NnOhV5HTQgoOsGmra1A1EByyHfzAGFcANB9FMxpBZsM50+leqf6SmcvnpQmCMguQ6P4DBxDENg99ZWRIjoxwzDMIdiGAZ2b21BlPuMIaEvmvhg/8dTVAwDwNAPdFjseZRhmCQY+oEPohAbN0OFWcE22cBhmAPoMQO7t7CRwzCMHEMX2PNpK8JBjm3HZC9s4DBS9JiBPVtb2chhGCaBuHETYONmyBEGhNAHvxXoImM2cBiSWFTvHcnh+XWGYcDGzXBj8BqcQcEqqhTpXW2vQKQYbNMwaCMhRgSfUwm1FKWiCityhYdFof3bWIVc5VSMXuWViBnYu7UVteMrYHdY4FWd8nyMSnm6Siu4eizycgXUTml6NOaXpuuEYsIfayWvHRVyJUdIk7uX7xFy1YkvWCJN74zQz9xjlauAPBb594abUFfZ5dnAodHKNasq7+iIrFIVUSWNmhM15LmFiJPCRLqfaEo9MfortScqz6xbl7elLqIO9qhytVQwRnvujejyeksFcNRUed2xWtzSdJdaQl7bgwpputcoJvKS152Y0KHrBvZta0M0GI1/GfeAVioGif4oDHl6TBAqKkJVFjPoa1P9LRmEM0uDbR7QqZmSTyHCIzjMEembruL5doYpTGLcBxQkTz75JMaMGQOHw4Hp06fjnXfeIY996qmncOaZZ6K0tBSlpaWoq6tLevxwwAYOMyAMvbeDC/nZGSDDFBLRSAy7t7SwWioDCAgIYZiwpT4CtWrVKjQ0NGDJkiV47733cNJJJ6G+vh7798v9Oq1btw5XXnkl/vSnP2H9+vUYNWoUvvjFL2LPnj2DfQxpwwYOM2AMQ2Dvtjb4faFMF4VhmGEgEopiz5ZWxFhskBEy6Qdn2bJlWLBgAebPn4/JkydjxYoVcLlcWLlypfT4F154AV//+tcxZcoUHHvssfj5z38OwzDQ2Ng42MeQNmzgMCkhhEDT9nZ0t8vn0RmGyQ9C/gj2bGmFnmRtEzPECGGOikoYMAwDPp8vYQuHCa/4kQg2btyIurq6eJqqqqirq8P69esHVPRAIIBoNIqyMrl37OGADRwmLfZ/1omO5u5MF4NhmCHA3xXC3m1tHDgzj9i8eTO8Xm/CtnTpUumxra2t0HUdVVVVCelVVVVoamoa0PW++93vora2NsFIGm5YRZUyB1RU5Gp8YvV+kkdNx02Rp1NqgygV+yVJLKoQEWPGKuTltSoH9TbtTd2IRnRUjiqBl4iXZNdLyWs7hLxc3ao8tk7AKleqhAx5TKuoLn8eABCO9UjTY4SqJqTJr9FDxPpxCnk6ALjDcgWLOyRXqDk1+btwqPLvE6tGf7dQIbJURa5wIpJBTekbSeb6I8SPZZQI0Bgy5OlBXd7G/ET9790nV8cFVfl7DRFx0qg6RSn5AEBV5Bo1m0X+vh2qvO64FHlbKjJKyGsXK8Q1LPIyRQ2BrlY/Wvck3n+QiOEUSvLMI8Q+SsFI9Wup9o9AMlVUqrGo+tIzY+j1eTI2I6dJkyZhw4YNCal2O632HAwPPfQQXnrpJaxbtw4OB/37M9SwgcMMiu72APSojqPGlUNV6WCHDMNkP617utDVKpe1MxngwCLhQWcDAVVVUVws/7g6nIqKCmiahubm5oT05uZmVFdXJz330UcfxUMPPYQ//vGPOPHEE9MusxnwFBUzaALdYezlhYgMk7MYhkDTp+1s3GQZvSM4+qC3VP3g2Gw2TJ06NWGBcN+C4RkzZpDnPfzww7j//vuxdu1aTJs2Ld3bNg0ewWFMIRKKYc+WVlSNKYPDTTuaYxgmu4hFdDRtb2cZOJNAQ0MD5s2bh2nTpuHUU0/F8uXL4ff7MX/+fADA3LlzMXLkyPg6nh/84AdYvHgxXnzxRYwZMya+Vsfj8cDjkS87GGrYwGFMQ4/1ejqtOMqLojL53D/DMNlDyB9B844OVkplLcKcKao0/ODMmTMHLS0tWLx4MZqamjBlyhSsXbs2vvB4165dUA9ZB/jTn/4UkUgEX/nKVxLyWbJkCe65555BlT9d2MBhTEUIgZbPOhEORlFeO7D5XoZhhh9fa6DfYmImuxDCHAMn3UXSCxcuxMKFC6X71q1bl/D3jh070rrGUMIGTqr0xaKCfAW/UOTLmpLFolKIpVCpx6iSv84waDm3RpyjEVXDYsjLalETVVSdrT0IhSIYNbYcGqHqcQj5sKVbJ9RVkBtMPku7ND2g0vGBooZ8rQGlkgnr8mdIHR8gYhYBQJfqkqbbVHmsIRvko2E2ncgnJle0AYCFUMdpRDQqlYhGZRAdpp4kbk5MkbeBCFHPI0Qso4hKqAWJdwoAMV2el24QCh1CDUm1VbuFHoK3Eu/VpcpVUcWG3G9IkZC/bypWGQBYJDI4IYC9n3VIfVkFiOcRIBRRQYV+5mEhbzMxIXcUmmrMqWR9KhmLilRXEYqsPuMiY7GomMHABg4zZIR6Itjzn1ZUjS6F3cXrchgm08SiOvbv7EB3j9yYYLKLPk/Gg4X6IMl32MBhhhQ9qmPv1laU1RbDWyH/kmUYZugJdIfRsqsTBuF3iMk+hElrcAp1BIoNHGZYaN/rQ9gfQcWoEvaXwzDDiBBAR3M3uvbLnVsyWYwwIMTg3W+Y4yww92ADhxk2/F0hhIMtqDyap6wYZjiIRXXs39WJsF++joVh8pmscvQnhMDixYtRU1MDp9OJuro6bNmyJek5f/nLXzB79mzU1tZCURSsWbOm3zHXXXcdFEVJ2GbOnDlEd8EkIxbpnbLq5K9JhhlSejpD2L25hY2bHKYvVMNgt0yFmsg0WTWC8/DDD+NHP/oRnnvuOYwdOxZ333036uvr8dFHH5HxLPx+P0466SR89atfxaWXXkrmPXPmTDzzzDPxv9ONwdEXdl4hV+OnFqMKAAxBKK+Ilf2U2iBKKLgUVa6QAQBVkY+kUPFzKFWNolPTTvL8m/Z2ofzAlJXFlngtBxHXyh2TV1cPoSbyoYQoExCwyBUeQbVTmh4WcrWIrssVITHiHSXbF4Zcsku+C1X+PKh3CgAKpZYirpEqRpLhdGqo3aCUTIQShrpGOsPwGvGsrJp8vZhdkac71RLyGi6jSJpebMiVVx4q9phF3r4PX14hDIG2PV3o6QgiFJM/q25d/sx7COVaQJF/kIRAf6hEiJhTUYOI50W0CzoWFd2nph9z6rDjD/T3ImMGQub84OQDWWPgCCGwfPly3HXXXbjooosAAL/4xS9QVVWFNWvW4IorrpCed/755+P8888/Yv52u/2IMTSY4SXkj2DPf1pQfpQXnhJ2DMgwgyXkj6D1s04Om5InmOYHp0ANnKyZotq+fTuampoSQqt7vV5Mnz4d69evH3T+69atQ2VlJSZNmoSbb74ZbW20nxJm+BCGQOuuTuzfyd5UGSZdhOhdyN+0rY2NG4Y5QNaM4PTFrehzA91HVVVVfF+6zJw5E5deeinGjh2Lbdu24c4778T555+P9evXQ9PkQ/PhcBjh8MEhU5/PN6gyMMkJdIUQ6omgfKQXnlL5dCTDMP0JB6Jo/awT0TDHkso/hCkKqMxNsWWWjI3gvPDCC/EgXB6PB9Eo4UnSBK644gpceOGFOOGEE3DxxRfjtddewz/+8Y9+rqYPZenSpfB6vfFt1KhRQ1Y+phdDN9CyqwMtOzugR3k0h2GSIQyBjr0+7NvaysZNntLnB2fQW4HKxDNm4Fx44YXYtGlTfKuoqAAANDc3JxzX3Nxs+tqZcePGoaKiAlu3biWPWbRoEbq6uuLbZ599ZmoZGJpAVwh7Nu9Hj8SVPMMwQKg7jL3/aYGvlQ6VwDCFTsamqIqKilBUdFBdIIRAdXU1GhsbMWXKFAC900IbNmzAzTffbOq1d+/ejba2NtTU1JDH2O32tJVWzOARhkDb7i74O4IoG+mF1ZE1s6kMkzGMmIH2vT74O+VKJCbPODACM/h8CnOKKmt+NRRFwa233ooHHngAEydOjMvEa2trcfHFF8ePO++883DJJZfEI5z29PQkjMRs374dmzZtQllZGY4++mj09PTg3nvvxZe//GVUV1dj27ZtuP322zFhwgTU19enUdLeYJsQclk0FYQzWRVVCJm4TgSZowL+KYRMPJpkoO7QcPeJ6fK1SeS1iSCcSWIvkhRZD1475I9g739a4B3hQXl1ERSJF2SnRV7WYp2WS/tjcomuX5RL03tUQiarytdmUbJyIPVgg5QsOqZTUvT8iDNE1WdNlb9XTaU/SCyKfF0XJft2EIFdPYS0223QAU7dFnl57UQQWo3wuNDR6kfHPh+Ekfhj5Sek4EASOTgh4far8nobImTiEYOWiUcNKsApFVRTnk71g5QUHKAl5EcMqtk/p74DyGsNLQLJfz1SyafwyBoDBwBuv/12+P1+3HDDDejs7MQZZ5yBtWvXJvjA2bZtG1pbW+N/v/vuuzj33HPjfzc0NAAA5s2bh2effRaapuGDDz7Ac889h87OTtTW1uKLX/wi7r//fh6hySG6WnoQ9gVRWlMMl5cl5UzhEAlE0L6nC/7A0K1TZLITs2JRFeoi46wycBRFwX333Yf77ruPPGbHjh0Jf59zzjlJnRg5nU784Q9/MKuITAbRowZad3XC7g6grLYYVgeHe2DyFz2mo7OpG/4Ono5imHTIKgOHYQZC2B/Bvi2t8JS5UFJdBBDTbAyTiwhDwNfaA1+Lv990FFNomCMT5ykqhskxetoD8HcG4a7woLjCLV2fwzC5RE97AD0t3ewmgQFgnidjDtXAMDmIMAQ6m7rR3epHSXURPKVOQGFDh8ktgr4QOpq6EQ3FQIScYgoSgbSUGv0oTIOZDZyUEb3/p8gtYioIZ7JKSgaNI5RJOgh1FREwkVKjAEBEEOcQ6USsTdKjUrKvDxGTB8nUhXxtTZFFXl3dVgUQBnr2dSHU3oOSqiK4vE44CC/VAFBklRc4TKhh/FF5WYNGmTQ9JOgIziFVvqYiTKRHQQT0JBR7SdUlZFBB4j1R748K7JpMsUeq/+Tv1UIEarWCUESBXnzuMOT7HJC/bycRyNTtkN+DPcnooUoY3IYQCPsj6GzyIRzo1TraNcAflfct3TH5e/UTSimADp5JqaWCCqUKJFRUhBoLAHRBqaXkbYNSS9FBV5M5N6T62+RBNfvvEPEjmNyDDRwmr4iFdbTu6oTV0YOy6mI4iznsA5N9RAIRtDf5EOqhDWGGAUzyg1OgBhobOExeEg3F0LqzA1aHBcWVHpaWM1lB2B+Bb383Qj0R6AW6LoJJBfaDMxjYwGHymmgohrZdneiyd6N4hAfuEl6jwww/oe4wfC09CPt5xIZJAWGSgVOgxjQbOExBEAvraN/dha7mbhRVeOAqdbLqihlShBAIdIXgb/UjGuJgmAwz3LCBwxQUetRA5z4f2pt8KCp3oajCDY0I9cAw6WDoBno6Auhu9UOPGtB4xJBJE3HI/x9cPjyCwwyI3qpCqaXMHAkUhOrEIGNXDUMMIkokQ/ThyZxUGUSji+ny9TLUmoWoIBQvSfS2TouCQFsAgbYAXF4HPOVu2N02eIhRHQ+Rl05cO6LTYUCCujz+UcSQP6uoQcSiIp6HnuSZU/v0VKWoRD3XQBuLGlF5NKI+WwjDwErESbMlcfjotMn32YjAT1Q8KAoDQDQcQ0+bH/6OIIQhoACwqAqCMfnD8sfk78JPqKV6CJVRQKGVTAEihhQVW4pSS4WJmFMxIt4UAET1VGOuUfGjUowrlfScdNVVHIsqF2EDhyl4Al0hBLpCsDos8Fa44SpxQuGvbmZACAR9Yfja/KyIYpgsgw0chjlANBRDx54udO7zwVXihKfMxfGuGCl6VIe/I4Ce9gCMmAG9MD+QmSFHmDQtUJgVlA0chjkMYQj42wPwtwdgdVjgLnXB6XVCYxezBY0wBILdIfjbA6yGYoaNQl0/YwZs4DBMEqKhGDr3+dCx1wdnsR2uEiecxQ6ewiogQj1hBDqDCHSFClZuyww/v/rVSybmJvCb36wxMb/cgA0chhkgQV8YQV8YiqrAWeyAy+uAzWNnvzp5SDgQQbArBH9nEMYhi4H5VTPDxb59e1FTUwMckLWkjwAg8Nlnn5lTsByCDZx0ob7kFCqmD11BKaWRASpGVbKCZQahyNUJBpGebJ9O3LduyONBRaNyxVJEp6t3SJdPN7mIaSjnIbIaYYjeL/rOIBw2BY4iB5xeBxyHGDtOC/2+iwy5CsggFUjydUAGUQWTrQeJESdReaVKMtdCFmInpVhK1U1RsglESmB16H1HAhEEfSEEu4IIhuSNLEAoogKEIgoAgsS+ABFjKUDEcAoQcaWCijyuFACEQMSQIlRRVGwpSi1FKaUAWi1Fx5wi0klFVJKOkNxH9c/ZNzJXXV2N3lqtA9CQnpHTF6xTwVFHHWVi6XIDNnAYZhAY+kFjR1EVODx2OIrscBU7oPKanaxGGAKh7jCC3SGEusMJIzUMkw10d3ehqKgI6Y/i9BpunZ0dZhYrZ2ADh2FMQhiidwTAF0LX3i5YnVY4i+ywe+ywueRRq5nhJRaOIdQTRqg7hIg/Ap1tGiaL8Xg8+NnPfoYbbrgBvQZOKkZOrw+dJ554Al6vd2gKmOUoQmTh2FwW4vP5DlSS3qFChapoxCS9ksQBGhS5nako8qkJlTheVeU/ohqR3rtPPr1j1eRRuC2qfJrIpsid89kUD3ltB+T7nELuCM8l5Nd2KfJ7cKm0/e4kRlcGMkV1KDbitR4+vaJqCuxuO+weGyxOO6z2/mVL9beWp6gOO16Spsd0hHsiiAbCCPvD0KOJT5m67wgxsxokHm7OTVERDv1ya4qKdvQH6hzKoSXxM5jo6E9HV1cXiouL6esOAbFYDFarFb01PJVRYQOAgUgkcuD8woNHcBhmGDD0g6M7ugGoFhV2lw12jw12l4397ZhELBJDOBBBxB9ByB+BfsBS0Xi2kMlRLBYLXnvtNXzpS1/CwEdxekdvfv3rXxescQOwgcMwGcGIGXGDBwAUVYHFaYXNZYXNaYPNaYVm5RhZyTB0A5FgFJFABJFgFLFABAZ73GPykAsuuAC9ho0BJJsNiNM7anjJJZcMYamyHzZw0oRyvqQQ/SulMkp6DnE8ORieyfUEaXwhC0Jxpqvy4eUYMewcFfLpsbAun2YDgJAhr/rBmDzdQQwB2Il0RxIVlTQski6gBiMIBSMIoXfKQdUUWJ1WOJxWWB1WWB0WWOyW+DRoesJR+VlmmQVmqqgPLZMeiSEaiiEaiiIUiCIaivabbqKmlQAgQMxmhAhVVJhYnBOi0onpJgAIEvU2pMind4KqfJooRExFRUSSeFDEPrOmopLFvxvyqSjieGAwU1HZiaIoePfdf2DatGk48oLjXln43/72t4L318UGDsNkKYYuEO6JIHaY11yLTYPFboHVboXFZoFm02C1W6Bac3sexogJ6JEYYhEdsXAMkXAUsXAMeiSW8LvEgzRMITJ16lQMbBTHAKDgtNNOG5ZyZTNs4DBMjhGL6IhFdIS7+38JazYNFpsG1aLBYtWgWlRoVg2aRYVqUaFaNCjDbAcJo3dKztAN6DEdRrT3v3pUhx41oEdi0KN6v4/rbP+qZpjhZvv2TzF27FjQozi9ozf/+c9/hrdgWQobOAyTR+gRPb6wNhmqRYWqKlA0FaqmAKrS+7eqAErvkLiiKPE+tG+oOy66FAf+feC/whAwDAEYgDAMGLro/W/MyEYfagyTk4wZMwbJR3F6R28mTpw4nMXKWtjAYZgCxIgZB5Zs9RpDZo2WkO4TGIYxhfb2NpSVlaHXmDl0ONYAILB///7MFCwLye1Je4ZhGIYpIEpLS9H7091r0PQi0GfwjBgxIlNFyzrYwGEYhmGYHCIU6lO7iYT/BgJyB46FCk9RmUyq8nEAEMROhfTGKSeT8nEq8J2h0t5GdWKfDiJdkadHIZeqhgnvygAQNuT77Lrc67OdkJXbY3I1gy1KfzvYCNe9VsJ1L5WuEfVGSyINpbwGm6UmTeYYnVI/6cQ51PFRwv0wlQ4AESKziCGvt2EhX8cUJtpkGHLpMwCECNl3GPL0iEKkE5JvSgoOAFFDnhcl744REu5UvRIDQy8HTzqtmqNy8IFit9vx0ksv4YorrkDfmpxnn30WTifd5xUiPILDMAzDMDnG5ZdffuBfvcb4Nddck7nCZCk8gjNADn6ZpvcFkPSslGUm1PHUVws9hEONvNDp8i9bKt0g0nv3yb/MDGIEh0rXhXwURU/iKyIm5La9RjxDjXAcphLXUAz624FyviVSdMJHjcakM4Jj2trgJFXZvBEceToVZ6v3HGLUh6jnMaLeUs4mY0lGcHRBjIoQ55D1nxjhoNpR777U2iudnlo/kfwcop+i+sG0RmNS6yOPjDhQlOwZAVIUBX/+859x9tln4w9/+AM0jT2fHw4bOAOku7v7wL+GYr4ntakoagfV9pKW+MiKYoZhGAa9vwPZFJn7rLPOyiqjK9vgaOIDxDAM7N27F0VFRVnj/trn82HUqFH47LPPhj3C7VCQT/eTT/cC8P1kM/l0L0B23o8QAt3d3aitrYWq8sqOXIFHcAaIqqo46qijMl0MKcXFxVnTEZhBPt1PPt0LwPeTzeTTvQDZdz/ZNHLDDAw2RRmGYRiGyTvYwGEYhmEYJu9gAyeHsdvtWLJkCex2e6aLYgr5dD/5dC8A3082k0/3AuTf/TCZgxcZMwzDMAyTd/AIDsMwDMMweQcbOAzDMAzD5B1s4DAMwzAMk3ewgZNlCCGwePFi1NTUwOl0oq6uDlu2bEl6zl/+8hfMnj0btbW1UBQFa9as6XfMddddB0VREraZM2cO0V0cZKjuJ518B0u613zyyScxZswYOBwOTJ8+He+8807C/nPOOaffu7npppuG6jYGXK7DeeWVV3DsscfC4XDghBNOwBtvvJGwPxPvpA+z7yVT7aWPVO7n3//+N7785S9jzJgxUBQFy5cvH3SeZmP2/dxzzz393s+xxx47hHfA5CSCySoeeugh4fV6xZo1a8Q///lPceGFF4qxY8eKYDBInvPGG2+I733ve2L16tUCgPjNb37T75h58+aJmTNnin379sW39vb2IbyTXobqftLJNxP38tJLLwmbzSZWrlwp/v3vf4sFCxaIkpIS0dzcHD/m7LPPFgsWLEh4N11dXUN2HwMt16H87W9/E5qmiYcfflh89NFH4q677hJWq1X861//ih+TiXcyVPeSqfaSzv2888474tvf/rb41a9+Jaqrq8UPf/jDQedpJkNxP0uWLBH/9V//lfB+WlpahvhOmFyDDZwswjAMUV1dLR555JF4Wmdnp7Db7eJXv/rVgPJIZuBcdNFFJpV0YAzV/ZiRb6qke81TTz1VfOMb34j/reu6qK2tFUuXLo2nnX322eKWW24ZknIPplyHcvnll4tZs2YlpE2fPl3ceOONQojMvJM+zL4XITLTXvpI9X4OZfTo0VKDYDB5DpahuJ8lS5aIk046ycRSMvkIT1FlEdu3b0dTUxPq6uriaV6vF9OnT8f69esHnf+6detQWVmJSZMm4eabb0ZbW9ug80zGUN3PUD8ns64ZiUSwcePGhHNUVUVdXV2/c1544QVUVFTg+OOPx6JFixAIBIbkPlItVx/r169POB4A6uvr48dn4p0AQ3MvfQx3ewHSu59M5JkN196yZQtqa2sxbtw4XH311di1a9dgi8vkGRyLKotoamoCAFRVVSWkV1VVxfely8yZM3HppZdi7Nix2LZtG+68806cf/75WL9+PTRNG1TeFEN1P0P5nMy8ZmtrK3Rdl57zySefxP++6qqrMHr0aNTW1uKDDz7Ad7/7XWzevBmrV682+S5SK9ehNDU1Jb33TLwTYGjuBchMewHSu59M5Jnpa0+fPh3PPvssJk2ahH379uHee+/FmWeeiQ8//BBFRUWDLTaTJ7CBk0FeeOEF3HjjjfG/X3/99SG71hVXXBH/9wknnIATTzwR48ePx7p163DeeeeZco3hvJ+hZjjv5YYbboj/+4QTTkBNTQ3OO+88bNu2DePHjx+y6zI0w9FemPQ5//zz4/8+8cQTMX36dIwePRovv/wyrr/++gyWjMkmeIoqg1x44YXYtGlTfKuoqAAANDc3JxzX3NyM6upqU689btw4VFRUYOvWrablOVz303fuUD4nM+6loqICmqalXM7p06cDgKnvZrDlqq6uTnr8cLwTGUNxLzKGor3ISLfODHee2XbtkpISHHPMMUP+fpjcgg2cDFJUVIQJEybEt8mTJ6O6uhqNjY3xY3w+HzZs2IAZM2aYeu3du3ejra0NNTU1puU5XPczduzYIX9OZtyLzWbD1KlTE84xDAONjY1Jy7lp0yYAMPXdDLZcM2bMSDgeAN5888348cPxTmQMxb3IGIr2IiPdOjPceWbbtXt6erBt27Yhfz9MjpHpVc5MIg899JAoKSkRv/3tb8UHH3wgLrroon5S2y984QviiSeeiP/d3d0t3n//ffH+++8LAGLZsmXi/fffFzt37ozv//a3vy3Wr18vtm/fLv74xz+Kz33uc2LixIkiFArl3P0MNN9suJeXXnpJ2O128eyzz4qPPvpI3HDDDaKkpEQ0NTUJIYTYunWruO+++8S7774rtm/fLn7729+KcePGibPOOmvI7mMg5br22mvFHXfcET/+b3/7m7BYLOLRRx8VH3/8sViyZIlUJj7c72Qo7iWT7SWd+wmHw/H2UlNTI7797W+L999/X2zZsmXAeeba/XzrW98S69atE9u3bxd/+9vfRF1dnaioqBD79+8f8vthcgc2cLIMwzDE3XffLaqqqoTdbhfnnXee2Lx5c8Ixo0ePFkuWLIn//ac//UkA6LfNmzdPCCFEIBAQX/ziF8WIESOE1WoVo0ePFgsWLBiWzm0o7meg+WbDvQghxBNPPCGOPvpoYbPZxKmnnirefvvt+L5du3aJs846S5SVlQm73S4mTJggvvOd7wy5H5wjlevss89OeN5CCPHyyy+LY445RthsNvFf//Vf4vXXX0/Yn4l30oeZ95LJ9tJHKvezfft2aXs5++yzB5znUGP2/cyZM0fU1NQIm80mRo4cKebMmSO2bt06bPfD5AYcTZxhGIZhmLyD1+AwDMMwDJN3sIHDMAzDMEzewQYOwzAMwzB5Bxs4DMMwDMPkHWzgMAzDMAyTd7CBwzAMwzBM3sEGDsMwDMMweQcbOAzDMAzD5B1s4DAMAwB4+umn8cUvfnHIr7N27VpMmTIFhmEM+bUYhilc2MBhGAahUAh33303lixZMuTXmjlzJqxWK1544YUhvxbDMIULGzgMw+DVV19FcXExTj/99GG53nXXXYcf/ehHw3IthmEKEzZwGCaPaGlpQXV1Nb7//e/H0/7+97/DZrOhsbGRPO+ll17C7NmzE9LOOecc3HrrrQlpF198Ma677rr432PGjMEDDzyAuXPnwuPxYPTo0fjd736HlpYWXHTRRfB4PDjxxBPx7rvvJuQze/ZsvPvuu9i2bVv6N8swDJMENnAYJo8YMWIEVq5ciXvuuQfvvvsuuru7ce2112LhwoU477zzyPPeeustTJs2La1r/vCHP8Tpp5+O999/H7NmzcK1116LuXPn4pprrsF7772H8ePHY+7cuTg0ru/RRx+Nqqoq/PWvf03rmgzDMEeCDRyGyTMuuOACLFiwAFdffTVuuukmuN1uLF26lDy+s7MTXV1dqK2tTft6N954IyZOnIjFixfD5/PhlFNOwWWXXYZjjjkG3/3ud/Hxxx+jubk54bza2lrs3LkzrWsyDMMcCTZwGCYPefTRRxGLxfDKK6/ghRdegN1uJ48NBoMAAIfDkda1TjzxxPi/q6qqAAAnnHBCv7T9+/cnnOd0OhEIBNK6JsMwzJFgA4dh8pBt27Zh7969MAwDO3bsSHpseXk5FEVBR0fHEfP9/9u5Q5VFojCM448fqCBi8wIsCpPU5hUYzGLQpN6DiFEsWgbDeAETbILFLGoQMU8b0WATwSaC7IZll2913YVVN5z9/9LwDuc9Jz68h5nr9XpXCwaDP54DgcDD2u1n4cfjUfF4/I97AsDfIOAAhrlcLqpUKiqVSmq326rX63fTk89CoZAsy5LneXfvbq+VNpvNS854Pp/l+74ymcxL+gHALQIOYJhWq6XT6aR+v69Go6FkMqlqtfrbNfl8XovF4q4+Ho81Go3k+746nY48z9Nut9N+v3/qjMvlUuFwWLlc7qk+APAIAQcwyHQ6lW3bcl1XsVhMHx8fcl1X8/lcg8Hg4bparabJZKLT6fRTvVAoqNvtyrIszWYzOY6j1Wol13WfOudwOFS5XFYkEnmqDwA8Evjy+dtNAP+tYrGobDarZrMp6dt/cNLptGzbfuk+h8NBqVRK6/VaiUTipb0B4DsmOAAkSb1eT9Fo9O37bLdbOY5DuAHwVkxwAPzSuyY4APAvEHAAAIBxuKICAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMb5CiksusjkWMj7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sim_data2.plot_field(\"xyFieldMon\", \"E\", \"abs\", f=ed_freq)\n",
    "ax.set_title(\"Electric dipole\")\n",
    "\n",
    "ax = sim_data2.plot_field(\"xyFieldMon\", \"E\", \"abs\", f=eq_freq)\n",
    "ax.set_title(\"Electric quadrupole\")\n",
    "\n",
    "ax = sim_data2.plot_field(\"xyFieldMon\", \"H\", \"abs\", f=md_freq)\n",
    "ax.set_title(\"Magnetic dipole\")\n",
    "\n",
    "ax = sim_data2.plot_field(\"xyFieldMon\", \"H\", \"abs\", f=mq_freq)\n",
    "ax.set_title(\"Magnetic quadrupole\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Optical scattering and far-field radiation",
   "Plasmonics"
  ],
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